Abstract
Objectives
Several epidemiologic studies have suggested that certain paternal occupations may be associated with an increased prevalence of birth defects in offspring. Using data from the National Birth Defects Prevention Study, we investigated the association between paternal occupation and birth defects in a case-control study of cases comprising over 60 different types of birth defects (n = 9998) and non-malformed controls (n = 4066) with dates of delivery between 1997 and 2004.
Methods
Using paternal occupational histories reported by mothers via telephone interview, jobs were systematically classified into 63 groups based on shared exposure profiles within occupation and industry. Data were analyzed using Bayesian logistic regression with a hierarchical prior for dependent shrinkage to stabilize estimation with sparse data.
Results
Several occupations were associated with an increased prevalence of various birth defect categories, including: mathematical, physical and computer scientists; artists; photographers and photo processors; food service workers; landscapers and groundskeepers; hairdressers and cosmetologists; office and administrative support workers; sawmill workers; petroleum and gas workers; chemical workers; printers; material moving equipment operators; and motor vehicle operators.
Conclusions
Findings from this study might be used to identify specific occupations worthy of further investigation, and to generate hypotheses about chemical or physical exposures common to such occupations.
Keywords: Bayes theorem, congenital abnormalities, occupational exposure, occupations, paternal exposure
Birth defects are a leading cause of infant mortality and developmental disabilities in the United States, yet the causes of most birth defects are unknown.[1, 2] Previous epidemiologic studies have suggested that certain paternal occupations and workplace exposures may be associated with an increased prevalence of birth defects in offspring.[3-5] Occupations found to be associated with various defects include: agricultural and groundskeeping workers, electronic industry workers, forestry and logging workers, janitors and cleaners, laboratory workers, painters, printers, vehicle manufacturers and mechanics, welders, and woodworkers.[6-21]
Investigations of occupation as a risk factor for birth defects face a number of methodological challenges.[4] Many of these challenges stem primarily from small sample size, which is impacted by low exposure prevalence (e.g., diversity of occupations) and the rarity of individual phenotypes. To recover statistical power, investigators may group occupations or birth defects into larger categories, tolerating increased heterogeneity in order to increase sample sizes within groups. Conversely, study designs with more finely categorized (and thus more homogeneous) exposures and outcomes are prone to other issues such as imprecision and multiple hypotheses testing.
The objective of this study was to use data from a large national case-control study of birth defects to explore the relation between paternal occupation and various birth defects using analytic methods that specifically address the statistical challenges associated with analysis of sparsely distributed data across numerous occupational groups and birth defect categories. A complementary analysis of the association between maternal occupation and birth defects in this study population has been previously published.[22]
METHODS
All data are from the National Birth Defects Prevention Study (NBDPS), an ongoing, multi-center, population-based case-control study designed to investigate a range of potential risk factors for major birth defects. Detailed study methods have been previously published.[23] Briefly, eligible cases with one or more major birth defect were identified by 10 participating state birth defect surveillance systems (Arkansas; California; Georgia; Iowa; Massachusetts; New Jersey; North Carolina; New York; Texas; Utah) and included live births, fetal deaths and prenatally diagnosed elective terminations. Controls were randomly selected in each state among live births without major defects from either hospital records or birth certificates, with an approximate overall case:control ratio of 3:1. Between 6 weeks and 24 months after the estimated delivery date (EDD), mothers were enrolled and interviewed by telephone in either English or Spanish using a structured questionnaire that covers numerous demographic, behavioral and clinical factors before and during pregnancy.
This analysis includes cases and controls with EDDs between 01 October 1997 and 31 December 2004. Overall participation in the interview among case and control mothers during this time period was 69.4 and 66.1%, respectively.
Outcome classification
Clinical geneticists at each center performed standardized case review and coding to determine eligibility.[24] Eligible cases of major defects (those typically considered to have major medical or surgical significance) were centrally reviewed again by NBDPS clinicians to confirm eligibility and to classify each case as having only one major birth defect (“isolated”), more than one major birth defect (“multiple”), or a pattern of defects that represent a complex developmental sequence. In general, only cases with non-syndromic isolated and multiple defects were considered for this analysis, which includes more than 60 distinct birth defect categories. The birth defects of interest in this study (Table 1) are grouped by the primary organ system affected; this grouping is for convenience of presentation only and is not indicative of shared etiology or embryological development.
Table 1.
Distribution of major birth defect categories eligible for analysis, by primary organ system, National Birth Defects Prevention Study, United States, 1997-2004
| Birth defect categoriesa | Isolated and multiple (n) |
Isolated only (n) |
|---|---|---|
| Non-heart defects | ||
| Amniotic Band Syndrome and limb body wall defects | ||
| Limb anomalies only | 70 | 66 |
| Craniofacial disruptions +/− limb anomalies | 19 | 14 |
| Body wall complex +/− limb anomalies and +/− craniofacial disruptions | 15 | 13 |
| Central nervous system defects | ||
| Neural tube defects | ||
| Anencephaly and craniorachischisis | 192 | 176 |
| Spina bifida | 425 | 383 |
| Encephalocele | 83 | 60 |
| Hydrocephaly | 178 | 128 |
| Cerebellar hypoplasia / Dandy-Walker malformation | 61 | 36 |
| Holoprosencephaly | 46 | 33 |
| Eye and ear defects | ||
| Cataracts | 99 | 92 |
| Anophthalmos / microphthalmos | 90 | 50 |
| Glaucoma / anterior chamber defects | 47 | 38 |
| Anotia / microtia | 242 | 184 |
| Orofacial defects | ||
| Oral clefts | ||
| Cleft palate | 562 | 455 |
| Cleft lip | 353 | 331 |
| Cleft lip + cleft palate | 658 | 574 |
| Choanal atresia | 55 | 27 |
| Gastrointestinal defects | ||
| Esophageal atresia | 252 | 112 |
| Duodenal atresia / stenosis | 70 | 47 |
| Jejunal or ileal atresia / stenosis | 154 | 133 |
| Colonic atresia / stenosis | 16 | 14 |
| Anorectal atresia / stenosis | 366 | 181 |
| Biliary atresia | 66 | 58 |
| Genitourinary defects | ||
| Hypospadias (2nd or 3rd degree only) | 751 | 689 |
| Bilateral renal agenesis or hypoplasia | 65 | 45 |
| Musculoskeletal defects | ||
| Bladder exstrophy | 30 | 27 |
| Limb deficiencies | ||
| Longitudinal limb deficiency | 153 | 84 |
| Longitudinal preaxial limb deficiency | 87 | 32 |
| Transverse limb deficiency | 247 | 213 |
| Intercalary limb deficiency | 24 | 18 |
| NOS limb deficiency | 9 | 7 |
| Craniosynostosis | 436 | 400 |
| Diaphragmatic hernia | 290 | 229 |
| Omphalocele | 147 | 94 |
| Gastroschisis | 379 | 350 |
| Sacral agenesis or caudal dysplasia | 21 | 3 |
| Heart Defects | ||
| Laterality defects with congenital heart diseaseb | 113 | |
| Conotruncal defects | ||
| Truncus arteriosus | 34 | 30 |
| Interrupted aortic arch Type B | 9 | 6 |
| Interrupted aortic arch NOS | 3 | 3 |
| Tetralogy of Fallot | 391 | 311 |
| d-Transposition of the great arteries | 250 | 239 |
| Double outlet right ventricle - transposition of the great arteries | 14 | 12 |
| Double outlet right ventricle - other | 15 | 10 |
| Ventricular septal defect, conoventricular | 31 | 25 |
| Atrioventricular septal defect | 68 | 56 |
| Anomalous pulmonary venous return | ||
| Total anomalous pulmonary venous return | 94 | 86 |
| Partial anomalous pulmonary venous return | 15 | 13 |
| Left-sided obstructions | ||
| Hypoplastic left heart syndrome | 195 | 179 |
| Interrupted aortic arch Type A | 6 | 5 |
| Coarctation of the aorta | 195 | 170 |
| Aortic stenosis | 109 | 102 |
| Right-sided obstructions | ||
| Pulmonary atresia | 56 | 52 |
| Pulmonary valve stenosis | 363 | 345 |
| Ebstein anomaly | 43 | 41 |
| Tricuspid atresia | 25 | 21 |
| Septal defects | ||
| Ventricular septal defect, perimembranous | 512 | 453 |
| Ventricular septal defect, muscular | 127 | 113 |
| Ventricular septal defect, NOS | 15 | 12 |
| Ventricular septal defect, OS | 9 | 7 |
| Multiple ventricular septal defects | 32 | 27 |
| Atrial septal defect, secundum or NOS | 614 | 473 |
| Atrial septal defect, OS | 3 | 1 |
| Single ventricleb | 146 | |
| Associated heart defects | ||
| AS + CoA | 30 | 26 |
| CoA + VSD | 87 | 74 |
| VSD + ASD | 281 | 222 |
| VSD + ASD + CoA | 32 | 23 |
| PVS + ASD | 60 | 53 |
| PVS + VSD | 56 | 46 |
Abbreviation: NOS, not otherwise specified; OS, other specified; AS, aortic stenosis; CoA, coarctation of the aorta; VSD, ventricular septal defect; ASD, atrial septal defect; PVS, pulmonary valve stenosis.
Defects are grouped by primary organ system and broad categories of heart defects for ease of presentation only; groupings do not necessarily represent shared etiology or embryological development.
All cases are considered to be complex sequences; classification of “isolated vs. multiple” not applicable.
Occupational classification
During the interview, mothers reported the job title, main activities and other details for each job held by the infant’s father for at least 1 month duration from 3 months preceding the estimated date of conception (EDC) through the EDD. Jobs were then coded using the Standard Occupational Classification (SOC) Manual and North American Industry Classification System (NAICS).[25, 26]
We restricted the exposure period of interest to 3 months preceding the EDC through the first month of pregnancy, which corresponds to the primary critical window of susceptibility for male-mediated mechanisms of teratogenesis.[5, 27] All jobs held during this period were further grouped by SOC and NAICS codes into a modified occupational classification scheme which combines occupations considered to have similar physical and chemical exposure profiles.[22, 28] For example, the occupational group “farmers and farm workers” included agricultural workers, farmers and ranchers, and floral designers. Fathers who held more than 1 job during the critical period were assigned to more than 1 occupational group as appropriate (up to 2 groups per job). For example, Air Force pilots were assigned to “armed forces” as well as “aircraft operators.”
Study sample
The overall study population included fathers of 14,920 singleton cases and 5,771 singleton controls conceived without donor sperm or embryo. We further excluded fathers with missing occupational histories (498 [3.3%] cases; 182 [3.2%] controls), fathers who did not work at all between 3 months before the EDC and the EDD (1024 [6.9%] cases; 321 [5.6%] controls), fathers who did not have a job or were students during the exposure period of interest (3318 [22.2%] cases; 1180 [20.4%] controls), and fathers who worked during the relevant period but whose job descriptions were insufficient for classification (82 [0.5%] cases; 22 [0.4%] controls). The final sample for analysis consisted of fathers of 9998 cases and 4066 controls.
Statistical analysis
Despite the large sample size overall, the data were sparsely distributed since the cross-classification of occupations and outcomes yielded over 5000 combinations, many of which were represented by very few or no father-infant pairs. Given this distribution as well as the underlying assumptions about common workplace exposures within occupational groups, we assumed that an occupation associated with one defect may be more likely to be associated with other defects, and likewise a birth defect associated with one occupation may be more likely to be associated with other occupations. Using Bayesian logistic regression with a heavy-tailed scale mixture of normals prior, we incorporated this data structure in a hierarchical prior for dependent shrinkage across coefficients for occupation. The prior corresponded to a t-distribution with low degrees of freedom, which is expressed as a gamma (Ga) precision mixture of normals centered at zero. In particular, we specify βkj ~ N(0, λj−1 νk−1),over the occupations k and birth defects j, with λj and νk assigned Ga(5,5) hyperpriors. Note that by fixing these parameters at their prior mean values λ j=v k=1, this prior is simply a N(0,1) prior which does not borrow any information across occupations or outcomes. A N(0,1) prior is relatively non-informative for log odds ratios (ORs), expressing our prior belief that ORs > 7.4 or < 0.14 are possible, but unlikely to be observed in our study. Closely-related priors are widely used due to their tendency for strong shrinkage of small coefficients towards zero, leading to a parsimonious model while limiting shrinkage of larger coefficients to reduce bias in estimating the signal.[29] These priors exhibit good performance in simulation studies and have previously been used to borrow information across related outcomes in high-dimensional regression.[30] In our study, shrinkage occurs in 2 dimensions: toward the typical OR for each birth defect and also toward the typical OR for each occupational group, accounting for multiplicities and dimensionality through an intrinsic Bayes correction via the hierarchical structure of the model. Thus, an advantage of this flexible modeling approach is that it is directly informed by the observed data: the degree of shrinkage is adaptively increased or decreased depending on the underlying data structure. Regression analyses were conducted using MATLAB version 7.7.0 (MathWorks Inc., Natick, MA; 2008).
An a priori set of potential confounding factors obtained from the interview included maternal residence at delivery (i.e., study center), maternal age, maternal race/ethnicity, maternal education, use of supplemental folic acid or prenatal vitamins, maternal smoking and alcohol use. Though some paternal characteristics were available (e.g., paternal race/ethnicity), maternal characteristics were chosen for adjustment because paternal information was often missing. For these covariates, we used independent N(0,2) prior distributions, which are relatively non-informative.
The primary referent group was a fixed referent consisting of the combined occupational groups “managers, administrators” and “salesworkers,” which were considered to have little or no chemical exposure. Analyses were repeated using an alternative referent for each index occupational group consisting of all other groups combined, such that this revolving referent was different for each occupational group. Analyses were conducted first with all cases (cases with isolated or multiple defects) and then with cases of isolated defects only, since isolated and non-isolated defects may differ etiologically depending on the exposure and phenotype of interest. Adjusted posterior median ORs with 95% credible intervals (95% CI) are reported.
RESULTS
Table 2 presents the distribution of occupational groups among fathers by case-control status. The majority of fathers (90%) held only one job during the critical period of interest; thus most fathers (also 90%) were assigned to only one occupational group. Managers and administrators (10%), salesworkers (9%), and construction workers (9%) were the most common occupational groups for both cases and controls. Thirty-three groups were sparsely populated, each representing less than 1% of cases or controls. Of the 63 occupational groups in the coding scheme, only one group, metal miners, was unrepresented. Two groups, “managers and administrators” and “salesworkers,” were combined for subsequent analyses because job descriptions were similar and often resulted in assignment to both occupational groups (e.g., manager of an auto part sales department was assigned to both groups).
Table 2.
Distribution of assigned occupational groups among fathers with one or more jobs during the critical exposure perioda, National Birth Defects Prevention Study, United States, 1997-2004
| Occupational group | Cases | Controls | Total |
|---|---|---|---|
| n (%)b | n (%)b | nb | |
| Managers, administrators | 973 (9.7) | 436 (10.7) | 1409 |
| Business and financial specialists | 355 (3.6) | 184 (4.5) | 539 |
| Mathematical, physical, and computer scientists | 440 (4.4) | 191 (4.7) | 631 |
| Architects, drafters, designers | 73 (0.7) | 49 (1.2) | 122 |
| Surveyors, geologists, geoscientists | 19 (0.2) | 7 (0.2) | 26 |
| Engineers, science technicians | 255 (2.6) | 111 (2.7) | 366 |
| Biological scientists | 80 (0.8) | 28 (0.7) | 108 |
| Chemical scientists and pharmacists | 32 (0.3) | 20 (0.5) | 52 |
| Legal and social service workers | 231 (2.3) | 115 (2.8) | 346 |
| Teachers, librarians | 227 (2.3) | 120 (3.0) | 347 |
| Artists | 8 (0.1) | 0 | 8 |
| Entertainers, athletes | 106 (1.1) | 44 (1.1) | 150 |
| Media and communication workers | 42 (0.4) | 22 (0.5) | 64 |
| Photographers, photo processors | 20 (0.2) | 3 (0.1) | 23 |
| Health care practitioners | 85 (0.9) | 43 (1.1) | 128 |
| Dentists, dental assistants | 12 (0.1) | 6 (0.1) | 18 |
| Nurses, therapists, health technicians | 163 (1.6) | 63 (1.5) | 226 |
| Police, guards | 296 (3.0) | 127 (3.1) | 423 |
| Firefighters | 58 (0.6) | 23 (0.6) | 81 |
| Food service workers | 555 (5.6) | 178 (4.4) | 733 |
| Landscapers, groundskeepers | 273 (2.7) | 87 (2.1) | 360 |
| Janitors, cleaners | 257 (2.6) | 90 (2.2) | 347 |
| Laundry and dry cleaning workers | 13 (0.1) | 6 (0.1) | 19 |
| Personal service workers | 50 (0.5) | 20 (0.5) | 70 |
| Hairdressers and cosmetologists | 26 (0.3) | 8 (0.2) | 34 |
| Salesworkers | 874 (8.7) | 360 (8.9) | 1234 |
| Office and administrative support workers | 240 (2.4) | 82 (2.0) | 322 |
| Messengers | 58 (0.6) | 29 (0.7) | 87 |
| Shippers | 275 (2.8) | 109 (2.7) | 384 |
| Farmers and farm workers | 397 (4.0) | 169 (4.2) | 566 |
| Fisher, hunters and trappers | 2 (<0.1) | 1 (<0.1) | 3 |
| Forestry and logging workers | 20 (0.2) | 6 (0.1) | 26 |
| Sawmill workers | 57 (0.6) | 16 (0.4) | 73 |
| Construction workers | 884 (8.8) | 345 (8.5) | 1229 |
| Carpenters, wood workers | 233 (2.3) | 103 (2.5) | 336 |
| Electricians, electrical, and electronics workers | 395 (4.0) | 138 (3.4) | 533 |
| Vehicle manufacturing | 60 (0.6) | 31 (0.8) | 91 |
| Vehicle mechanics | 315 (3.2) | 112 (2.8) | 427 |
| Mechanics, NEC | 441 (4.4) | 156 (3.8) | 597 |
| Metal miners | 0 | 0 | 0 |
| Foundry and smelter workers | 31 (0.3) | 13 (0.3) | 44 |
| Petroleum and gas workers | 71 (0.7) | 20 (0.5) | 91 |
| Stone, glass, and concrete workers | 37 (0.4) | 21 (0.5) | 58 |
| Sheetmetal, iron, and other metal workers | 126 (1.3) | 55 (1.4) | 181 |
| Welders, cutters | 111 (1.1) | 35 (0.9) | 146 |
| Chemical workers, NEC | 114 (1.1) | 39 (1.0) | 153 |
| Food processing workers | 231 (2.3) | 88 (2.2) | 319 |
| Printers | 54 (0.5) | 23 (0.6) | 77 |
| Painters | 156 (1.6) | 80 (2.0) | 236 |
| Textile workers | 35 (0.4) | 16 (0.4) | 51 |
| Paper workers | 38 (0.4) | 14 (0.3) | 52 |
| Semiconductor processors | 7 (0.1) | 2 (<0.1) | 9 |
| Electronic equipment operators | 79 (0.8) | 29 (0.7) | 108 |
| Plant and system operators | 18 (0.2) | 9 (0.2) | 27 |
| Material moving equipment operators | 251 (2.5) | 93 (2.3) | 344 |
| Motor vehicle operators | 538 (5.4) | 200 (4.9) | 738 |
| Aircraft operators, air crew | 30 (0.3) | 8 (0.2) | 38 |
| Rail transportation workers | 15 (0.2) | 6 (0.1) | 21 |
| Water transportation workers | 8 (0.1) | 5 (0.1) | 13 |
| Transportation workers, NEC | 15 (0.2) | 3 (0.1) | 18 |
| Service station attendants | 25 (0.3) | 6 (0.1) | 31 |
| Armed forces | 170 (1.7) | 82 (2.0) | 252 |
| Commercial divers | 1 (<0.1) | 0 | 1 |
Abbreviation: NEC, not elsewhere classified.
Three months preceding the estimated date of conception through the first month of pregnancy.
The distribution does not add up to the total sample (9998 cases; 4066 controls) because each father may have more than one job, and each job may be assigned to more than one occupational group.
The main occupational analysis yielded over 20,000 effect measure estimates. A complete set of results is available from the corresponding author. To facilitate the presentation and interpretation of results, effect measure estimates are reported in Table 3 only for occupation-defect combinations with any exposed cases for which the 95% CI excluded the null (1.0) before rounding to 2 significant digits, or for which the observed effect estimate was ≥2.0 or ≤0.5 in either analyses with all cases (isolated and multiple defects) or with only cases of isolated defects. Thus, occupations were considered to be potentially associated with a defect if the effect estimate met these specified criteria. Since very similar results were observed using either referent group, results are presented only for analyses using the fixed referent category consisting of managers, administrators and salesworkers.
Table 3.
Adjusted posterior median odds ratios and 95% credible intervals for birth defects associated with occupational groups assigned to fathers with one or more jobs during the critical exposure perioda, National Birth Defects Prevention Study, United States, 1997-2004
| Occupational group | Associated defect categoryb | Isolated and multiple |
Isolated only | ||
|---|---|---|---|---|---|
| nb | OR (95% CI)d | nc | OR (95% CI)d | ||
| Managers, administrators; salesworkers | REF e | REF e | |||
| Business and financial specialists | Cleft palate | 14 | 0.8 (0.6, 1.0) | 14 | 0.8 (0.6, 1.0) |
| Jejunal or ileal atresia / stenosis | 2 | 0.6 (0.4, 1.0) | 2 | 0.6 (0.4, 1.0) | |
| Mathematical, physical, and computer scientists |
Anorectal atresia / stenosis | 7 | 0.7 (0.5, 0.9) | 7 | 0.7 (0.5, 0.9) |
| Colonic atresia / stenosis | 1 | 2.0 (0.8, 5.1) | 1 | 2.0 (0.8, 5.1) | |
| Limb deficiency, intercalary | 3 | 1.7 (0.9, 3.3) | 3 | 1.7 (0.9, 3.3) | |
| Diaphragmatic hernia | 21 | 1.2 (0.9, 1.6) | 21 | 1.2 (0.9, 1.6) | |
| Coarctation of the aorta | 3 | 0.6 (0.4, 0.9) | 3 | 0.6 (0.4, 0.9) | |
| Surveyors, geologists, geoscientists | Biliary atresia | 1 | 1.9 (0.8, 4.8) | 1 | 1.9 (0.8, 4.8) |
| Engineers, science technicians | Colonic atresia / stenosis | 1 | 2.1 (0.9, 5.8) | 1 | 2.1 (0.9, 5.8) |
| Chemical scientists and pharmacists | Interrupted aortic arch, Type B | 1 | 2.4 (0.7, 8.5) | 1 | 2.4 (0.7, 8.5) |
| Legal and social service workers | Esophageal atresia | 14 | 1.3 (0.9, 1.7) | 14 | 1.3 (0.9, 1.7) |
| ASD, secundum or NOS | 2 | 0.7 (0.5, 1.0) | 2 | 0.7 (0.5, 1.0) | |
| Teachers, librarians | Duodenal atresia / stenosis | 5 | 1.3 (0.8, 2.0) | 5 | 1.3 (0.8, 2.0) |
| Artists | Encephalocele | 1 | 21 (1.8, 3000) | 1 | 21 (1.8, 3000) |
| Cataracts | 1 | 17 (1.3, 5000) | 1 | 17 (1.3, 5000) | |
| Anophthalmos / microphthalmos | 1 | 25 (2.0, 7000) | 1 | 25 (2.0, 7000) | |
| Anotia / microtia | 1 | 14 (1.3, 1000) | 1 | 14 (1.3, 1000) | |
| Cleft palate | 2 | 11 (1.6, 609) | 2 | 11 (1.6, 609) | |
| Cleft lip | 1 | 10 (1.1, 687) | 1 | 10 (1.1, 687) | |
| Anorectal atresia / stenosis | 1 | 8.8 (0.9, 660) | 1 | 8.8 (0.9, 660) | |
| Bilateral renal agenesis or hypoplasia | 1 | 19 (1.6, 4000) | 1 | 19 (1.6, 4000) | |
| Limb deficiency, transverse | 1 | 15 (1.6, 1000) | 1 | 15 (1.6, 1000) | |
| Hypoplastic left heart syndrome | 2 | 18 (2.5, 1000) | 2 | 18 (2.5, 1000) | |
| ASD, secundum or NOS | 1 | 6.1 (0.7, 431) | 1 | 6.1 (0.7, 431) | |
| Media and communication workers | Colonic atresia / stenosis | 1 | 2.8 (0.9, 9.6) | 1 | 2.8 (0.9, 9.6) |
| Photographers, photo processors | ABS: limb anomalies only | 1 | 2.1 (0.8, 6.7) | 1 | 2.1 (0.8, 6.7) |
| Cataracts | 1 | 1.9 (0.6, 8.5) | 1 | 1.9 (0.6, 8.5) | |
| Anophthalmos / microphthalmos | 1 | 2.3 (0.8, 7.3) | 1 | 2.3 (0.8, 7.3) | |
| Glaucoma / anterior chamber defects | 1 | 3.2 (0.9, 16) | 1 | 3.2 (0.9, 16) | |
| Hypospadias | 4 | 2.0 (0.9, 4.6) | 4 | 2.0 (0.9, 4.6) | |
| Nurses, therapists, health technicians | Colonic atresia / stenosis | 1 | 2.1 (0.8, 6.4) | 1 | 2.1 (0.8, 6.4) |
| Police, guards | Diaphragmatic hernia | 3 | 0.7 (0.5, 1.0) | 3 | 0.7 (0.5, 1.0) |
| Food service workers | Anotia / microtia | 20 | 1.3 (1.0, 1.8) | 20 | 1.3 (1.0, 1.8) |
| Biliary atresia | 5 | 1.6 (1.0, 2.5) | 5 | 1.6 (1.0, 2.5) | |
| Limb deficiency, transverse | 18 | 1.4 (1.0, 1.8) | 18 | 1.4 (1.0, 1.8) | |
| Gastroschisis | 49 | 1.4 (1.1, 1.8) | 49 | 1.4 (1.1, 1.8) | |
| Sacral agenesis or caudal dysplasia | 2 | 1.3 (0.7, 2.6) | 2 | 1.3 (0.7, 2.6) | |
| Landscapers, groundskeepers | ABS: Craniofacial disruptions +/− limb | 2 | 1.9 (0.9, 4.4) | 2 | 1.9 (0.9, 4.4) |
| Anencephaly | 8 | 1.4 (1.0, 2.1) | 8 | 1.4 (1.0, 2.1) | |
| Esophageal atresia | 8 | 1.2 (0.9, 1.8) | 8 | 1.2 (0.9, 1.8) | |
| Duodenal atresia / stenosis | 4 | 1.4 (0.8, 2.3) | 4 | 1.4 (0.8, 2.3) | |
| Biliary atresia | 3 | 1.7 (1.0, 2.8) | 3 | 1.7 (1.0, 2.8) | |
| TAPVR | 7 | 1.8 (1.2, 2.8) | 7 | 1.8 (1.2, 2.8) | |
| ASD, secundum or NOS | 18 | 1.3 (1.0, 1.7) | 18 | 1.3 (1.0, 1.7) | |
| Janitors, cleaners | ABS: Craniofacial disruptions +/− limb | 3 | 2.3 (1.1, 5.1) | 3 | 2.3 (1.1, 5.1) |
| Cleft lip | 12 | 1.4 (1.0, 2.0) | 12 | 1.4 (1.0, 2.0) | |
| Laundry and dry cleaning workers | Anophthalmos / microphthalmos | 2 | 2.4 (1.0, 5.8) | 2 | 2.4 (1.0, 5.8) |
| AS + CoA | 1 | 2.0 (0.7, 6.2) | 1 | 2.0 (0.7, 6.2) | |
| Personal service workers | Interrupted aortic arch Type B | 1 | 2.0 (0.6, 6.8) | 1 | 2.0 (0.6, 6.8) |
| Hairdressers and cosmetologists | Choanal atresia | 1 | 2.0 (0.7, 5.2) | 1 | 2.0 (0.7, 5.2) |
| Limb deficiency, longitudinal preaxial | 2 | 2.0 (0.9, 4.8) | 2 | 2.0 (0.9, 4.8) | |
| Gastroschisis | 3 | 2.0 (0.9, 4.3) | 3 | 2.0 (0.9, 4.3) | |
| VSD, conoventricular | 2 | 2.7 (1.0, 7.5) | 2 | 2.7 (1.0, 7.5) | |
| AVSD | 2 | 2.2 (0.9, 5.4) | 2 | 2.2 (0.9, 5.4) | |
| Office and administrative support workers | Glaucoma / anterior chamber defects | 6 | 2.5 (1.5, 4.5) | 6 | 2.5 (1.5, 4.5) |
| Colonic atresia / stenosis | 1 | 1.9 (0.7, 5.2) | 1 | 1.9 (0.7, 5.2) | |
| Hypospadias | 23 | 1.4 (1.0, 2.0) | 23 | 1.4 (1.0, 2.0) | |
| Bladder exstrophy | 3 | 2.0 (1.0, 3.6) | 3 | 2.0 (1.0, 3.6) | |
| Omphalocele | 8 | 1.5 (1.0, 2.2) | 8 | 1.5 (1.0, 2.2) | |
| Messengers | Choanal atresia | 1 | 1.5 (0.7, 3.1) | 1 | 1.5 (0.7, 3.1) |
| VSD, NOS | 2 | 2.6 (0.7, 12) | 2 | 2.6 (0.7, 12) | |
| Shippers | Cleft lip | 14 | 1.4 (1.0, 1.9) | 14 | 1.4 (1.0, 1.9) |
| Anorectal atresia / stenosis | 2 | 0.6 (0.4, 0.9) | 2 | 0.6 (0.4, 0.9) | |
| Farmers and farm workers | Anotia / microtia | 28 | 1.4 (1.1, 1.9) | 28 | 1.4 (1.1, 1.9) |
| Gastroschisis | 12 | 0.7 (0.5, 1.0) | 12 | 0.7 (0.5, 1.0) | |
| VSD, muscular | 1 | 0.5 (0.2, 1.0) | 1 | 0.5 (0.2, 1.0) | |
| Forestry and logging workers | ABS: Body wall complex | 1 | 2.7 (0.7, 11) | 1 | 2.7 (0.7, 11) |
| Sawmill workers | Glaucoma / anterior chamber defects | 1 | 1.9 (0.7, 5.1) | 1 | 1.9 (0.7, 5.1) |
| Choanal atresia | 1 | 2.0 (0.8, 4.8) | 1 | 2.0 (0.8, 4.8) | |
| Hypospadias | 5 | 1.8 (1.0, 3.3) | 5 | 1.8 (1.0, 3.3) | |
| Interrupted aortic arch, Type B | 1 | 2.6 (0.7, 9.8) | 1 | 2.6 (0.7, 9.8) | |
| AVSD | 2 | 1.8 (0.8, 3.9) | 2 | 1.8 (0.8, 3.9) | |
| Construction workers | Biliary atresia | 9 | 1.6 (1.1, 2.4) | 9 | 1.6 (1.1, 2.4) |
| Carpenters, wood workers | Colonic atresia / stenosis | 1 | 1.9 (0.8, 5.1) | 1 | 1.9 (0.8, 5.1) |
| Coarctation of the aorta | 1 | 0.6 (0.4, 1.0) | 1 | 0.6 (0.4, 1.0) | |
| Electricians, electrical, and electronics workers |
Cleft lip + cleft palate | 37 | 1.3 (1.0, 1.6) | 37 | 1.3 (1.0, 1.6) |
| Vehicle mechanics | Hypospadias | 22 | 1.4 (1.0, 1.9) | 22 | 1.4 (1.0, 1.9) |
| Mechanics, NEC | Biliary atresia | 4 | 1.5 (1.0, 2.5) | 4 | 1.5 (1.0, 2.5) |
| VSD & ASD | 23 | 1.5 (1.1, 1.9) | 23 | 1.5 (1.1, 1.9) | |
| Petroleum and gas workers | Glaucoma / anterior chamber defects | 1 | 2.0 (0.8, 5.1) | 1 | 2.0 (0.8, 5.1) |
| Colonic atresia / stenosis | 1 | 2.8 (0.9, 9.1) | 1 | 2.8 (0.9, 9.1) | |
| Limb deficiency, intercalary | 2 | 2.6 (1.1, 6.5) | 2 | 2.6 (1.1, 6.5) | |
| ASD, secundum or NOS | 11 | 1.6 (1.0, 2.4) | 11 | 1.6 (1.0, 2.4) | |
| Sheetmetal, iron, and other metal workers | Single ventricle | 5 | 1.6 (1.0, 2.6) | 5 | 1.6 (1.0, 2.6) |
| Welders, cutters | ABS: Body Wall Complex | 2 | 2.2 (0.8, 5.7) | 2 | 2.2 (0.8, 5.7) |
| Laterality defects with CHD | 5 | 2.1 (1.2, 3.5) | 5 | 2.1 (1.2, 3.5) | |
| Chemical workers, NEC | Anophthalmos / microphthalmos | 3 | 1.6 (0.9, 2.9) | 3 | 1.6 (0.9, 2.9) |
| Cleft lip | 8 | 1.6 (1.1, 2.4) | 8 | 1.6 (1.1, 2.4) | |
| Pulmonary valve stenosis | 10 | 1.5 (1.0, 2.2) | 10 | 1.5 (1.0, 2.2) | |
| Food processing workers | Encephalocele | 7 | 1.6 (1.0, 2.5) | 7 | 1.6 (1.0, 2.5) |
| Hydrocephaly | 10 | 1.5 (1.1, 2.2) | 10 | 1.5 (1.1, 2.2) | |
| Printers | Colonic atresia / stenosis | 1 | 3.0 (1.0, 9.6) | 1 | 3.0 (1.0, 9.6) |
| Double outlet right ventricle - other | 1 | 2.2 (0.8, 6.2) | 1 | 2.2 (0.8, 6.2) | |
| Tricuspid atresia | 2 | 2.4 (1.0, 5.4) | 2 | 2.4 (1.0, 5.4) | |
| VSD, NOS | 1 | 2.3 (0.5, 15) | 1 | 2.3 (0.5, 15) | |
| Textile workers | VSD, NOS | 1 | 3.1 (0.7, 25) | 1 | 3.1 (0.7, 25) |
| Material moving equipment operators | Cleft lip | 13 | 1.4 (1.0, 1.9) | 13 | 1.4 (1.0, 1.9) |
| Craniosynostosis | 18 | 1.4 (1.0, 1.9) | 18 | 1.4 (1.0, 1.9) | |
| ASD, OS | 1 | 2.3 (0.7, 9.8) | 1 | 2.3 (0.7, 9.8) | |
| Motor vehicle operators | Anencephaly | 17 | 1.4 (1.1, 1.9) | 17 | 1.4 (1.1, 1.9) |
| Anophthalmos / microphthalmos | 10 | 1.5 (1.1, 2.2) | 10 | 1.5 (1.1, 2.2) | |
| Glaucoma / anterior chamber defects | 5 | 1.7 (1.0, 2.8) | 5 | 1.7 (1.0, 2.8) | |
| Cleft lip + cleft palate | 42 | 1.2 (1.0, 1.5) | 42 | 1.2 (1.0, 1.5) | |
| Hypospadias | 35 | 1.3 (1.0, 1.7) | 35 | 1.3 (1.0, 1.7) | |
| Limb deficiency, transverse | 22 | 1.5 (1.1, 1.9) | 22 | 1.5 (1.1, 1.9) | |
| Aircraft operators, air crew | Anencephaly | 3 | 2.2 (1.1, 4.5) | 3 | 2.2 (1.1, 4.5) |
| Water transportation workers | ABS: Craniofacial disruptions +/− limb | 1 | 3.2 (0.9, 15) | 1 | 3.2 (0.9, 15) |
| Transportation workers, NEC | Colonic atresia / stenosis | 1 | 4.0 (1.0, 19) | 1 | 4.0 (1.0, 19) |
| Service station attendants | PVS & ASD | 1 | 2.5 (0.9, 7.0) | 1 | 2.5 (0.9, 7.0) |
Abbreviations: ABS, amniotic band syndrome; AS, aortic stenosis; ASD, atrial septal defect; AVSD, atrioventricular septal defect; CHD, congenital heart disease; CoA, coarctation of the aorta; CI, Credible Interval; NE, not estimated; NEC, not elsewhere NOS, not otherwise specified; classified; OR, Odds Ratio; OS, other specified; PVS, pulmonic valve stenosis; REF, referent; TAPVR, total anomalous pulmonary venous return; VSD, ventricular septal defect
Three months preceding the estimated date of conception through the first month of pregnancy.
Defect categories were considered to be associated with an occupation if the 95% credible interval around the odds ratio for occupation-defect combinations with any exposed cases excluded the null before rounding, or if the odds ratio was ≥2.0 or ≤0.5 for either isolated defects or for all cases combined.
Number of exposed cases.
Adjusted for maternal age at delivery, maternal race/ethnicity, maternal education, maternal smoking, maternal alcohol use, maternal vitamin/folic acid use, and maternal residence at delivery.
Results presented for analyses with the common referent consisting of two occupational groups combined, “Managers, Administrators” and “Salesworkers”.
Several occupations were positively associated with 3 or more birth defect categories: mathematical, physical and computer scientists; artists; photographers and photo processors; food service workers; landscapers and groundskeepers; hairdressers and cosmetologists; office and administrative support workers; sawmill workers; petroleum and gas workers; chemical workers not elsewhere classified (NEC); printers; material moving equipment operators; and motor vehicle operators.
Occupations associated with several different defects within the same anatomic system include artists, for which large effect estimates were observed for several defects of the oral cavity, eyes and ears, gastrointestinal system, limbs and heart. Photographers and photo processors were associated with 3 different eye defects: cataracts, anophthalmos/microphthalmos and glaucoma/anterior chamber defects. Motor vehicle operators were associated with anophthalmos/microphthalmos and glaucoma/anterior chamber defects. Landscapers and groundskeepers were associated with 3 gastrointestinal defects: esophageal atresia, duodenal atresia/stenosis and biliary atresia.
A number of occupations were associated with reduced odds of certain birth defects. For example, mathematical, physical and computer scientists were associated with reduced odds of anorectal atresia/stenosis as well as coarctation of the aorta. However, no occupation was associated with reduced odds of more than one defect within the same anatomic system.
Nearly one third of the occupational groups were not associated with any birth defect: architects, drafters and designers; biological scientists; entertainers and athletes; health care practitioners; dentists and dental assistants; firefighters; fishers, hunters and trappers; vehicle manufacturers; foundry and smelter workers; stone, glass and concrete workers; painters; paper workers; semiconductor processors; electronic equipment operators; plant and system operators; rail transportation workers; armed forces; and commercial divers.
DISCUSSION
This analysis provides a broad examination of the relation between paternal work in over 60 occupations and numerous birth defects. We observed over 100 occupation-defect combinations with effect measure estimates that met our pre-specified criteria for significance. Given the breadth of results and the potential for common etiologic pathways, one helpful summary approach is to examine the pattern of associated birth defects within each occupation. Several occupations were associated with more than one defect, suggesting the potential for diverse effects from a potential exposure or mixture of exposures represented by these paternal occupation categories. Some occupations were even associated with more than one defect within the same anatomic system, suggesting the possibility of varied effects from an early insult on morphogenesis, or perhaps differences in effects on components of an anatomic system depending upon timing or dose of a teratogenic exposure. For example, photographers and photo processing workers were associated with 3 distinct eye defects. Landscapers and groundskeepers were associated with 3 categories of gastrointestinal defects. Artists were associated with the most number of individual defects, including several eye/ear defects, oral clefts and defects of the gastrointestinal system. The artist group is particularly interesting as there are no exposed controls, which is reflected in the strikingly large odds ratios and upper limits of the credible intervals. To our knowledge, an increased prevalence of birth defects among offspring of artists has not been previously reported. However, some artists’ media may contain organic solvents or lead, both of which have been previously associated with birth defects in offspring of exposed fathers.[31, 32]
An alternative interpretive approach is to examine the pattern of associations across occupations that are thought to share specific exposures in the workplace. However, we did not have any information about specific agents to which fathers in our study population may have been occupationally exposed. Therefore, to better elucidate any potential patterns in exposures across occupational groups found to be associated with birth defects in these data, and to offer an additional perspective on interpreting our results, we employed an existing classification scheme determined by industrial hygienist review of job titles to identify occupations commonly exposed to solvents, wood and wood products, heavy metals and pesticides, which have each been associated with certain birth defects in previous studies of paternal occupation.[6] Occupations considered exposed to wood (e.g., sawmill workers) or metals (e.g., vehicle mechanics) were not observed to be associated with any consistent pattern of birth defects in our study. However, solvent-exposed occupations were significantly associated with an increased prevalence of numerous birth defects among NTDs, eye defects, oral clefts, gastrointestinal defects, limb deficiencies and heart defects. The following occupations were considered potentially exposed to solvents: artists, chemical workers, pharmacists, chemical engineers, electricians and electrical workers, janitors, mechanics, nurses, painters, dry cleaning and laundry workers, printers and plumbers. Associations between solvent-exposed paternal occupations and NTDs [12, 13, 32, 33] and other defects [6-8, 34]have been previously reported in many but not all [11, 15, 35] studies. We also observed an association between defects of the neural tube and both food processing workers andlandscapers/groundskeepers,, which is consistent with at least one other study reporting an association between pesticide-exposed occupations and NTDs.[16] In contrast to some previous studies, we did not observe an association with pesticide-exposed occupations and limb deficiencies.[21]
Only two population-based case-control studies in North America have previously examined a similar cross-classification of multiple paternal occupations and multiple birth defects. Olshan et al. [6] linked cases of birth defects from a Canadian surveillance registry to birth certificates, from which matched controls and information about paternal occupation were obtained. An occupational classification scheme similar to the one in our study was used, but there were important differences in the scope and definition of defects considered eligible. Standard statistical methods were applied and all results were presented without restriction by statistical significance or magnitude of the effect estimates. We did not observe any of the noted occupation-defect associations reported in the Canadian study such as janitors and ventricular septal defects (OR = 2.45; 95% CI = [1.10 to 5.45]; 13 exposed cases); forestry and logging workers and cataracts (2.28 [1.29, 4.02]; 30) and atrial septal defects (2.03 [1.35, 3.05]; 54); and painters and spina bifida (3.21 [0.91, 11.36]; 7) and cleft palate (3.36 [1.19, 9.46]; 9). However, our study yielded similar results for other elevated effects estimates (OR ≥ 1.5) observed by Olshan et al. such as shippers, messengers and cleft lip (1.50 [0.44, 5.12]; 5), chemical workers NEC and cleft lip (6.00 [0.62, 57.81]; 3), motor vehicle operators and cleft lip + cleft palate (1.49 [1.04, 2.11]; 58), and photographers, photo processors and hypospadias (2.00 [0.28, 14.23]; 2). These results, though consistent, are based on small numbers of exposed cases in both studies.
Schnitzer et al. [7] used self-reported job information from fathers in the Atlanta Birth Defects Case-Control Study, applied the same occupational classification scheme as Olshan et al., but considered additional birth defect categories. The authors presented results from conditional logistic regression analyses with an OR greater than 1.5 and at least 3 exposed cases. We observed only one association consistent with this study: food processors and hydrocephaly (3.3 [1.2, 8.8]; 6). A large Norwegian study linking national birth and occupation registries also examined multiple occupations and defects; the only common finding with this study was for vehicle mechanics and hypospadias (5.19 [1.31, 14.24]; 3).[36]
A recent analysis of maternal occupation and birth defects in the NBDPS (1997-2003) employed a similar study design and analytic approach.[22] Though the analysis of maternal occupation examined slightly different occupational groupings (given the different distribution of occupations between sexes), common findings are nevertheless potentially informative as they may point to teratogens encountered in the shared workplace. In both studies, solvent-exposed occupations were associated with defects of the neural tube, eye, limb, heart and gastrointestinal system as well as orofacial clefts. For example, the observed prevalence of amniotic bands and orofacial clefts was higher among offspring of both mothers and fathers who worked as janitors or cleaners. Admittedly, caution is warranted in making direct comparisons between studies of maternal and paternal occupation and occupational exposures. Given the expected variability in exposure patterns – even of the same physical or chemical agent – between men and women employed in the same occupation,[37] an association may be observed between a particular occupation and defect for one gender (e.g., the gender with the typically “higher” exposure level) but not the other. Further, because agents may demonstrate different mechanisms of teratogenesis and impact different target tissues following exposure during different time periods of susceptibility for men and women, it would not be unexpected if a particular occupational exposure had a true causal effect for one gender but not the other.
Direct comparison with other studies of paternal occupation and birth defects is also complicated by differences in study population, source and classification of occupation, and grouping of birth defects. For example, we cannot directly compare our results with studies that lump all birth defects together or that use very different occupational classifications. Further, this study is not designed to investigate any particular occupation-defect or exposure-defect relationship in depth, which would require an exposure assessment strategy beyond exclusive use of job titles. However, the results of our broad screening analysis suggested that paternal work in solvent-exposed occupations may be associated with an increase in the prevalence of several birth defects among offspring, including defects of the eye, NTDs and oral clefts. We also observed that a number of occupations (i.e., artists; photographers and photo processors; motor vehicle operators; landscapers and groundskeepers) were positively associated with several defects within the same anatomic system, which may suggest heterogeneity in effects depending on unmeasured exposure parameters such as timing or intensity.
Using the Bayesian shrinkage approach, we were able to improve upon many limitations of previous studies. For example, to reduce misclassification, we used finer, more homogenous groupings of both occupation and birth defects. Preserving the etiologic diversity of individual phenotypes is a major analytic challenge in birth defects research, and our adaptive methods use shrinkage in a manner that allows examination of more homogenous defect categories while also borrowing information across exposures and outcomes according to the presumed underlying structure of the observed data. Further, these methods address the problematic issues related to using a large number of finer exposure and outcome categories, such as decreased precision and multiple hypothesis testing.
Nonetheless, caution in the interpretation of the results is warranted. We found that when there were sufficient numbers of cases within a particular occupation, the model would allocate the coefficient for that occupation to the tails of the distribution and avoid shrinkage. However, when there were sufficient individuals in an occupation to rule out a zero log-odds ratio but not enough for reliable estimation of the coefficient, large point and interval estimates were sometimes obtained. For example, all artists in the study were fathers of cases, leading to inflated effect measure estimates and large upper interval bounds for this occupation. Further, although large effect measure estimates (OR≥2.0) were observed for several occupation-defect combinations, most were based on few exposed cases.
Other limitations associated with exposure assessment in our study should be considered when interpreting the results. First, paternal occupational histories were reported by mothers via interview. Although use of maternal reports likely introduced some error in our occupational classification, agreement between maternal and paternal report of paternal occupation has been shown to be high (up to 80%) within two years after birth.[38, 39] Further, such misclassification has been demonstrated to be non-differential with respect to case status and thus will generally bias the effect measure estimates towards the null.[40] Despite the potential error associated with maternal report of paternal job, the quality and completeness of paternal occupational histories obtained by maternal interview exceeds that obtained from birth certificates.[41, 42] Second, the process of coding reported jobs by occupation and industry, and the further classification into 63 aggregate occupational groups, likely introduced non-differential misclassification that may have resulted in attenuation of observed effects for any given occupation. Ultimately, observed associations should be interpreted with caution, as our study is limited to using occupational groups as a surrogate measure for workplace exposures and exposure mixtures potentially encountered at each job.
Lastly, we acknowledge that the length of time between the infant’s birth and the maternal interview could be a potential source of recall bias. Women were asked between 6 weeks and 24 months after the EDD to report on jobs held by their baby’s father during their entire pregnancy and the 3 months before conception. Although a pregnancy calendar was used to aid recall, an extended time-to-interview could contribute to increased errors in reporting, and could further lead to recall bias if systematic differences in reporting accuracy due to time-to-interview exist between cases of different defects. However, the average time-to-interview in our study was less than one year (11 months for cases and 9 months for controls), and ranged from only 9 to 14 months among cases of different defects. Further, mothers were asked about the fathers’ job title and general description of tasks, which is likely less susceptible to recall error than questions about specific chemical agents or other workplace exposures. Therefore, we do not expect time-to-interview to be a significant source of recall error or bias in our study.
Our exploratory study has a number of notable strengths that make it an important contribution to the existing literature on paternal occupation as a risk factor for birth defects. We used data from a national population-based case-control study with systematic case review and classification for a wide variety of birth defects among live-born infants, stillbirths and electively terminated pregnancies. We were able to examine more than 60 defect categories, many of which have not been previously investigated in relation to paternal occupation. Despite the aforementioned limitations related to classification of occupation, our study improves upon previous studies that have relied on data from birth certificates because we obtained detailed occupational histories by interview on multiple jobs held during the time window most etiologically relevant to male-mediated teratogenesis. We were able to account for potential confounding by several important covariates collected during the maternal interview. Further, consistency in observed effects across our 2 comparison groups – a fixed referent comprised of all managers, administrators and salesworkers, representing a set of jobs considered unlikely to have substantial chemical exposure; and a revolving referent consisting of all occupations other than the index occupation, an approach which is frequently used in similar studies – minimizes concern that our observed results are affected by residual confounding by factors such as socioeconomic status.[15] Finally, our adaptive Bayesian analytic approach allows for the estimation of associations between numerous relatively homogenous occupational groups and etiologically distinct categories of birth defects, thereby addressing common small sample analytic limitations and avoiding the need for further aggregation of either exposure or outcomes into larger, less homogeneous groups.
Our study contributes evidence to the growing body of epidemiologic literature on male-mediated teratogenesis. Findings from this broad screening analysis can be used to inform further investigation of specific paternal occupations found to be associated with birth defects, and to generate hypotheses about chemical or physical exposures and exposure mixtures common to such occupations.
What this paper adds.
Previous epidemiologic investigations of paternal occupation and birth defects in offspring have grouped etiologically distinct phenotypes together, which may introduce etiologic heterogeneity and dilute associations. Likewise, job titles with potentially different chemical and physical exposure profiles are often loosely grouped together by major industry rather than by shared exposures, leading to exposure misclassification.
This large, population-based study was conducted to explore the relation between multiple paternal occupations and over 60 types of birth defects using Bayesian analytic methods that specifically address the statistical challenges associated with analysis of sparsely distributed data across numerous exposures and outcomes.
Results from this study indicate that paternal work in a number of occupations may be associated with an increased prevalence of various birth defects in offspring. Findings can be used to inform future investigation of specific paternal occupations found to be associated with birth defects, or to generate hypotheses about chemical or physical exposures and exposure mixtures common to such occupations.
ACKNOWLEDGEMENTS
The authors thank Joanna Smith (University of North Carolina Gillings School of Global Public Health, Chapel Hill, North Carolina) for her invaluable programming support.
FUNDING This work was supported by the Centers for Disease Control and Prevention (cooperative agreement number U50CCU422096) and the US National Institute of Environmental Health Sciences (grant numbers T32ES007018 and P30ES10126). This manuscript has been approved for submission to Occupational and Environmental Medicine by the National Birth Defects Prevention Study and the National Center on Birth Defects and Developmental Disabilities.
Footnotes
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
COMPETING INTERESTS None declared.
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