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• W2897702297 abstract "Birth defects are the leading cause of medical expenditures, hospitalizations, and death within the first year of life (Christianson, Howson, & Modell, 2006; Martin, Kochanek, Strobino, Guyer, & MacDorman, 2005; Waitzman, Romano, & Scheffler, 1994; Yoon et al., 1997). Although some birth defects are caused by recognized syndromes, the etiology is unknown in the majority of patients with birth defects (Christianson et al., 2006), which often engenders uncertainty in parents about the risk for recurrence in subsequent pregnancies. Despite substantial and ongoing efforts, few genetic risk factors for nonsyndromic birth defects have been identified. Thus, in the absence of genetic screening and/or testing, family history continues to provide the most useful predictor of sibling recurrence, and empiric estimates of risk are used for clinical risk counseling. These sibling recurrence risk estimates vary depending on the specific birth defect (e.g., the risk ratio among siblings of affected cases compared to siblings of unaffected subjects is 45 for isolated cleft palate and seven for clubfoot) (Lie, Wilcox, & Skjaerven, 1994). If accurate and reliable, these estimates can allow for informed, evidence-based choices regarding future reproductive decisions among parents of affected individuals, and could be used to identify high-risk pregnancies for optimal treatment and care options (e.g., delivery and surgery planning). However, the limitations of existing estimates and resources for clinical sibling recurrence risk estimation call into question their accuracy and appropriateness for modern populations. First, many of the existing estimates of nonsyndromic birth defect sibling recurrence risk are old, including data from more than three decades ago (Table 1). This is, in part, due to the fact that enormous data sets (e.g., >1,000,000 births) are required to garner a sufficient number of recurrent cases to estimate recurrence risks with a high level of statistical precision (e.g., even a birth defect with a prevalence of 1 per 1,000 births and a sibling recurrence rate of 10% would be expected to result in only ~100 recurrent cases among sibling of 1,000,000 random index subjects with one sibling in a population). Although several European countries have large historical data sets (e.g., data since 1936 in Denmark) (Christensen & Mitchell, 1996), many of these sites have relatively few births per year, which makes it difficult to estimate modern recurrence risks in these populations. Second, many existing estimates may no longer be relevant, considering changes in population risk profiles over recent decades. For example, many existing estimates were based on time periods and populations without fortification of food products with folic acid, which was introduced in a number of countries only within the last two decades (Rader, 2002), and has resulted in a reduction of the prevalence of neural tube defects and changes in their risk profile (Ahrens, Yazdy, Mitchell, & Werler, 2011; Mosley et al., 2009). Thus, recurrence estimates generated among unfortified populations may not be applicable to fortified populations. Further, over the last 30 years, there have been additional shifts in the prevalence and distributions of major birth defect risk factors, such as maternal smoking and obesity. Although maternal smoking is associated with a number of birth defects (Nicoletti, Appel, Siedersberger Neto, Guimaraes, & Zhang, 2014; U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, & Office on Smoking and Health, 2014), smoking rates in many countries have decreased substantially since the years during which many existing recurrence risk estimates were generated (Ebrahim, Floyd, Merritt, Decoufle, & Holtzman, 2000; Stein, Ellis, Savitz, Vichinsky, & Perl, 2009). Conversely, the rates of maternal obesity, also associated with many birth defects (Block et al., 2013; Kim, Dietz, England, Morrow, & Callaghan, 2007; Marengo, Farag, & Canfield, 2013), have increased in recent years. It is unknown how changes in the distributions of these and other risk factors (e.g., environmental exposures, maternal behaviors, occupational exposures) may have influenced modern recurrence risks and rendered existing recurrence estimates inaccurate. Third, the majority of existing estimates have been generated among white populations, leaving an information gap among nonwhite populations. The extent to which birth defects recurrence risks vary by race/ethnicity is not clear, but given the known major differences in birth defect risk by race/ethnicity (Canfield et al., 2014), it may be that existing estimates derived among white populations are not applicable to other groups. Similarly, many of the existing estimates were derived among Northern European populations. Because the prevalence of many birth defects vary substantially by country or other geographic categories (Feldkamp et al., 2011; Langlois, Jandle, Scheuerle, Horel, & Carozza, 2010; Li et al., 2013), it is not clear if estimates from these populations are generalizable to external populations. Fourth, because the reliable estimation of recurrence risks for especially rare defects requires very large samples (e.g., across multiple countries/states), existing estimates have focused on relatively common defects, such as heart defects, orofacial clefts, and neural tube defects. To incorporate data on rare defects (e.g., <5 recurrent cases), some studies have evaluated recurrence only of very broad, lumped categories of birth defects (e.g., all defects of the “digestive organs” as opposed to “gastroschisis”). These estimates have limited utility for counseling on the recurrence of specific rare birth defects, as the etiologies of such defects are likely heterogeneous. In this editorial, we focus primarily on population-based approaches for estimating sibling recurrence risks. Although many studies have derived estimates from nonpopulation-based sources, particularly from patients recruited from clinics (Carter et al., 1982; Ives, Coffey, & Carter, 1980; Klotz et al., 2010; Pietrzyk, 1983), population-based approaches are preferable because clinic-based samples can be subject to selection bias related to volunteering to participate in research and access to care. For example, parents with multiple affected offspring might be more likely to attend a clinic, be referred to research, and be motivated to participate in research compared to the parent of a single affected offspring. Therefore, clinic-based samples may be disproportionately representative of patients with a strong family history and/or more severe phenotypes, which might lead to upwardly biased recurrence risk estimates. An additional advantage of population-based approaches is the availability of an unrelated control group of offspring without birth defects, which allows for improved estimation of relative risks over clinic-based studies. There are relatively few large population-based sibling registries, and most have been constructed for evaluating other pregnancy outcomes (Chapman & Gray, 2014; Herman et al., 1997; Zhu & Le, 2003). Therefore, there is a need for generating these resources to estimate modern birth defect recurrence estimates. Such efforts often necessitate collaboration across state/country birth defects registries. The calculation of reliable, modern birth defect sibling recurrence risk estimates requires enormous populations (e.g., >1,000,000 births). In many countries and states, such population-based data have only become available in recent years, in the form of state/country-wide birth defect surveillance systems. Many of these systems use passive, active, or hybrid case-finding methods within most hospitals, delivery centers, and/or midwife facilities within their jurisdiction. As it has been reported that even passive case-finding programs in the United States, which are suspected to have the lowest completeness, are capable of achieving a completeness of ascertainment for selected birth defects higher than 85% (Salemi et al., 2012), these state/country-wide systems are thought to identify most affected offspring delivered in the surveillance region. Birth defect registries are routinely linked to the vital records for affected cases. However, sibling recurrence risk estimation also requires the identification of siblings of cases, and many birth defects surveillance systems lack maternally linked vital records, and do not routinely link records between affected index cases and their sibling(s). Nonetheless, a number of population-based surveillance programs with large, diverse, and contemporary populations are working toward the integration of sibling linkages into their surveillance systems. For example, the Florida Birth Defects Registry has already implemented a maternally linked file, which links sibling(s) with the same mother together. However, the complete linkage between an index subject and all of their half- and full-sibling(s) can be performed based on matching both of the parents to all of their offspring. Parent–offspring linkages would typically be performed based on matching parental information (e.g., name, date of birth, address) included in each subjects' birth certificate or other vital record. In fact, the use of vital records also allows for the identification of random (presumably unaffected) index control subjects and their sibling(s), which can be used for the estimation of relative sibling recurrence risks (Khoury, Beaty, & Liang, 1988). For example, control index subjects without birth defects (i.e., subject not present in a birth defects registry) could be randomly identified among vital records and linked to both of their parents and, subsequently, all of their sibling(s). Various deterministic and probabilistic methods exist for constructing reliable maternally linked databases, which are the foundation for sibling linkages (Blakely & Salmond, 2002; Clark & Hahn, 1995; Jaro, 1995; Mason & Tu, 2008; Meray, Reitsma, Ravelli, & Bonsel, 2007; Salemi, Tanner, Bailey, Mbah, & Salihu, 2013; Tromp, Ravelli, Bonsel, Hasman, & Reitsma, 2011). For example, there are data linkage functions that can automatically detect and assign probabilities to matches with missing or transposed letters/numbers (e.g., “Martha” versus “Marha” or “Marhta”) (Grannis, Overhage, & McDonald, 2004; Sauleau, Paumier, & Buemi, 2005). We have demonstrated the use of a hierarchical, deterministic approach—in which linkages based on higher confidence matches (e.g., complete names, full social security number) can be implemented before lower confidence matches (e.g., matches with different maternal last names or partial matches due to spelling variation) —to achieve high linkage rates (>92%) and minimize false positive matches when linking vital records to hospital discharge data for infants less than 1 year of age (Salemi et al., 2013). Despite the limitation of potential selection bias among studies with case identification through nonpopulation-based methods (e.g., clinics), studies with direct family contact may be helpful for establishing a more complete family history, including collecting data on early sibling terminations and pregnancy losses as well on extended family members (which might not be present in a population-based surveillance system). Existing data are available from ongoing consortium efforts for a number of relatively common defects, such as spina bifida or heart defects (Hoang et al., 2018; Thibadeau, 2017). However, the accuracy and detail of birth defect diagnoses in relatives based on self-report by a proband's parents may be low compared to review of medical records by birth defect registry staff. Although it might be optimal to combine population-based case identification and medical record review with direct assessment of family history from parents, such an approach is likely beyond the capacity of most birth defects surveillance systems. Many existing sibling recurrence risk estimates for nonsydromic birth defects are dated and may not be generalizable to modern populations. Existing data from vital records and birth defect registries offer a great opportunity to generate sibling birth defect registries that could be used to update these estimates and generate new estimates for rare defects and for common defects among population subgroups (e.g., among minority groups). Generating these recurrence risk estimates would allow for improved reproductive counseling, which would better empower parents of affected offspring to make more informed reproductive decisions. It would also be helpful for estimates across various sources to be compiled (e.g., in textbooks or review articles), with indicators of precision (e.g., 95% confidence interval), citations for the original data sources, and comments on potential limitations of the original studies. Further, more accurate recurrence risk prediction tools would also lead to improved screening and identification of recurrent cases, which would result in improved care and treatment for these patients (e.g., better delivery planning). In addition, population-based sibling birth defect registries would allow for evaluating additional research questions regarding etiologic factors that vary across pregnancies, such as maternal exposure changes between pregnancies, which are also understudied. As population-based sibling birth defect surveillance systems would not be immune from other registries' lack of data on early pregnancy losses and terminations (e.g., before 20 weeks of gestation), innovative strategies for including all pregnancy outcomes may be key to maximizing the utility of resulting recurrence risk estimates. Also, contemporary sibling registries will not be a panacea to the current recurrence risk information gap considering their limited applicability to rare, multiplex families with multiple affected extended relatives. Nonetheless, current efforts to modernize birth defect recurrence risks represent an important step towards birth defect prevention and improved genetic counseling. None." @default.
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• W2897702297 title "Using birth defects surveillance programs for population‐based estimation of sibling recurrence risks" @default.
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