Interrelation of the Common Congenital Malformations. Some Aetiological Implications

Roberts, Colin; Powell, R G

doi:10.1097/00006254-197605000-00011

Cited by 5 publications

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“…As can be seen in Table IV, all but the NTD + CL associations in this second column are nonrandom. Table IV also includes the analyses of our data using the Khoury et al method (third column) as well as the results observed in the Atlanta population (fourth column) by Khoury et al [19901. DISCUSSION Several different methods have been proposed for analyzing the patterns of congenital anomaly clusters [Roberts and Powell, 1972;Kallen, 1988;Khoury et al, 19901. For those defects that belong t o the same sequence (such as SB and CF) or to the same DFD (such as AN and TEA or SB and H Y ) , the associations are specific and nonrandom, either as an isolated association (first column) or as part of a more generalized MCA pattern (second column).…”

Section: Resultsmentioning

confidence: 99%

“…Different methods for analyzing patterns of congenital anomaly clusters have been proposed [Roberts and Powell, 1972;Kallen, 1988;Khoury et al, 19901, but most of them have been criticized because large O/E ratios are obtained for many defect combinations, suggesting a generalized and nonspecific tendency for the association of congenital defects. The method is tested on the Spanish Collaborative Study of Congenital Malformations (ECEMC) data base.…”

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Epidemiological analysis of the association between two congenital anomalies in the same child: A method for adjusting nonspecific clustering

Prieto

Martínez‐Frías

1996

Am. J. Med. Genet.

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Most of the methods proposed to analyze patterns of congenital anomaly clusters have been criticized because large observed/expected (O/E) ratios are obtained for many defect combinations, suggesting that the association of congenital defects is a generalized and nonspecific tendency. To avoid this problem, two methods have been proposed: (1) the analysis of the association of two defects in the subgroup of infants with only multiple congenital anomaly (MCA) patterns, and (2) a method for adjusting the O/E ratio for the nonspecific tendency of defects to cluster among themselves. However, neither of these methods analyzes the association of only two particular defects in a child who apparently has no other anomalies apart from the two that are being analyzed. Here we present a method that analyzes the association of two particular defects, when they are the only defects observed in the child. The method is tested on the Spanish Collaborative Study of Congenital Malformations (ECEMC) data base. The proposed method also controls for the tendency of nonspecific defect clustering.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Epidemiological analysis of the association between two congenital anomalies in the same child: A method for adjusting nonspecific clustering

Prieto

Martínez‐Frías

1996

Am. J. Med. Genet.

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“…However, birth defects tend to co‐occur more often than predicted by chance alone (Roberts & Powell, ; reviewed in Agopian et al, ), a phenomenon referred to as generalized and nonspecific clustering, which is known to lead to inflated O/E ratios (Khoury et al, ). The method proposed by Khoury et al () addresses nonspecific clustering by adjusting the O/E ratio of the defects of interest (e.g., defect A, defect B) with any defect using the following equation (Appendix A, eq.…”

Section: Methodsmentioning

confidence: 99%

Co‐occurring defect analysis: A platform for analyzing birth defect co‐occurrence in registries

Benjamin

Sanchez

et al. 2019

Birth Defects Research

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Background Few studies have systematically evaluated birth defect co‐occurrence patterns, perhaps, in part, due to the lack of software designed to implement large‐scale, complex analytic methods. Methods We created an R‐based platform, “co‐occurring defect analysis” (CODA), designed to implement analyses of birth defect co‐occurrence patterns in birth defect registries. CODA uses an established algorithm for calculating the observed‐to‐expected ratio of a given birth defect combination, accounting for the known tendency of birth defects to co‐occur nonspecifically. To demonstrate CODA's feasibility, we evaluated the computational time needed to assess 2‐ to 5‐way combinations of major birth defects in the Texas Birth Defects Registry (TBDR) (1999–2014). We report on two examples of pairwise patterns, defects co‐occurring with trisomy 21 or with non‐syndromic spina bifida, to demonstrate proof‐of‐concept. Results We evaluated combinations of 175 major birth defects among 206,784 infants in the TBDR. CODA performed efficiently in the data set, analyzing 1.5 million 5‐way combinations in 18 hr. As anticipated, we identified large observed‐to‐expected ratios for the birth defects that co‐occur with trisomy 21 or spina bifida. Conclusions CODA is available for application to birth defect data sets and can be used to better understand co‐occurrence patterns. Co‐occurrence patterns elucidated by using CODA may be helpful for identifying new birth defect associations and may provide etiological insights regarding potentially shared pathogenic mechanisms. CODA may also have wider applications, such as assessing patterns of additional types of co‐occurrence patterns in other large data sets (e.g., medical records).

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“…However, in general, most pairs of birth defects co‐occur in nonspecific patterns more often than expected by chance, resulting in O:E ratios >1.00 (Roberts and Powell, ; Jarvik et al, ). To address this issue, modifications of the O:E ratio approach have been proposed to assess more specific clustering.…”

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Analytic Methods for Evaluating Patterns of Multiple Congenital Anomalies in Birth Defect Registries

Agopian

Evans

Lupo

2017

Birth Defects Research

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Background It is estimated that 20 to 30% of infants with birth defects have two or more birth defects. Among these infants with multiple congenital anomalies (MCA), co‐occurring anomalies may represent either chance (i.e., unrelated etiologies) or pathogenically associated patterns of anomalies. While some MCA patterns have been recognized and described (e.g., known syndromes), others have not been identified or characterized. Elucidating these patterns may result in a better understanding of the etiologies of these MCAs. Methods This article reviews the literature with regard to analytic methods that have been used to evaluate patterns of MCAs, in particular those using birth defect registry data. Results A popular method for MCA assessment involves a comparison of the observed to expected ratio for a given combination of MCAs, or one of several modified versions of this comparison. Other methods include use of numerical taxonomy or other clustering techniques, multiple regression analysis, and log‐linear analysis. Advantages and disadvantages of these approaches, as well as specific applications, were outlined. Conclusion Despite the availability of multiple analytic approaches, relatively few MCA combinations have been assessed. The availability of large birth defects registries and computing resources that allow for automated, big data strategies for prioritizing MCA patterns may provide for new avenues for better understanding co‐occurrence of birth defects. Thus, the selection of an analytic approach may depend on several considerations. Birth Defects Research 110:5–11, 2018. © 2017 Wiley Periodicals, Inc.

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Interrelation of the Common Congenital Malformations. Some Aetiological Implications

Cited by 5 publications

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Epidemiological analysis of the association between two congenital anomalies in the same child: A method for adjusting nonspecific clustering

Epidemiological analysis of the association between two congenital anomalies in the same child: A method for adjusting nonspecific clustering

Co‐occurring defect analysis: A platform for analyzing birth defect co‐occurrence in registries

Analytic Methods for Evaluating Patterns of Multiple Congenital Anomalies in Birth Defect Registries

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