Identifying Algorithms to Improve the Accuracy of Unverified Diagnosis Codes for Birth Defects

Salemi, Jason L.; Rutkowski, Rachel E; Tanner, Jean Paul; Matas, Jennifer L.; Kirby, Russell S.

doi:10.1177/0033354918763168

Cited by 14 publications

(20 citation statements)

References 19 publications

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“…This finding, that algorithms designed to vary the number of diagnoses, medical encounters, and data sources can lead to an increase in PPV at the expense of case ascertainment has been reported previously. 15 Similarly, our findings that birth certificate flags have relatively high PPVs but miss a substantial number of confirmed cases is similar to other studies that report high PPVs for birth certificates but sensitivities ranging from 13% to 69%. 1,2,4,5 Our PPV results are consistent with several studies which reported PPVs of 37% to 63% but differ from those of Salemi et al, based on the Florida birth defects registry.…”

Section: Discussionsupporting

confidence: 91%

“…Restricting to inpatient codes improved PPVs but not substantially, and while the combination of a diagnosis code plus a procedure code resulted in high PPVs, this approach missed more than half of confirmed NTDs. This finding, that algorithms designed to vary the number of diagnoses, medical encounters, and data sources can lead to an increase in PPV at the expense of case ascertainment has been reported previously 15 . Similarly, our findings that birth certificate flags have relatively high PPVs but miss a substantial number of confirmed cases is similar to other studies that report high PPVs for birth certificates but sensitivities ranging from 13% to 69% 1,2,4,5 …”

Section: Discussionsupporting

confidence: 90%

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Validity of diagnosis and procedure codes for identifying neural tube defects in infants

Cheetham

Dublin

Pocobelli

et al. 2020

Pharmacoepidemiology and Drug

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Purpose: The use of validated criteria to identify birth defects in electronic healthcare databases can avoid the cost and time-intensive efforts required to conduct chart reviews to confirm outcomes. This study evaluated the validity of various case-finding methodologies to identify neural tube defects (NTDs) in infants using an electronic healthcare database. Methods: This analysis used data generated from a study whose primary aim was to evaluate the association between first-trimester maternal prescription opioid use and NTDs. The study was conducted within the Medication Exposure in Pregnancy Risk Evaluation Program. A broad approach was used to identify potential NTDs including diagnosis and procedure codes from inpatient and outpatient settings, death certificates and birth defect flags in birth certificates. Potential NTD cases were chart

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 90%

Validity of diagnosis and procedure codes for identifying neural tube defects in infants

Cheetham

Dublin

Pocobelli

et al. 2020

Pharmacoepidemiology and Drug

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“…Diagnoses made by the TBDR registry result from protocols that include active case‐finding, detailed medical record review and abstraction by surveillance specialists, and case classification by clinical geneticists. In contrast, diagnoses in the NIS rely exclusively on ICD‐based diagnoses without the possibility of any subsequent verification, resulting in suboptimal completeness and accuracy with considerable variation across specific defects (Salemi et al, ; Salemi, Rutkowski, Tanner, Matas, & Kirby, ). Missed diagnoses and false positive diagnoses are more likely with less severe birth defects; therefore, compared to the TBDR, the NIS may include a slight under‐representation of defects that would ultimately be classified as “not severe” in this study.…”

Section: Discussionmentioning

confidence: 99%

Exploring classification of birth defects severity in national hospital discharge databases compared to an active surveillance program

Matas

Agana

Germanos

et al. 2019

Birth Defects Research

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Objective To explore the extent to which the severity of birth defects could be differentiated using severity of illness (SOI) and risk of mortality (ROM) measures available in national discharge databases. Methods Data from the 2012–14 National Inpatient Sample (NIS) was used to identify hospitalizations with one or more major birth defects reported annually to the National Birth Defects Prevention Network using the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD‐9‐CM) diagnosis codes. Each hospitalization also contained a 4‐level SOI and 4‐level ROM classification measure. For each birth defect and for each individual birth defect‐related ICD‐9‐CM code, we calculated mean and median SOI and ROM, the proportion of hospitalizations in each level of SOI and ROM, the inpatient mortality rate, and level of agreement between various existing or derived severity proxies in the NIS and the Texas Birth Defects Registry (TBDR). Results Mean SOI ranged from 1.5 (cleft lip alone) to 3.7 (single ventricle), and mean ROM ranged from 1.1 (cleft lip alone) to 3.9 (anencephaly). As a group, critical congenital heart defects had the highest average number of co‐occurring defects, mean SOI, and ROM, whereas orofacial and genitourinary defects had the lowest SOI and ROM. We found strong levels of agreement between TBDR severity classifications and NIS severity classifications defined using Level 3 or 4 SOI or ROM Level 3 or 4. Conclusions This preliminary investigation demonstrated how severity indices of birth defects could be differentiated and compared to a severity algorithm of an existing surveillance program.

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“…Another limitation is that the algorithms used for BCCASS might not be applicable in other jurisdictions as they are dependent on local databases and jurisdiction-specific ICD coding practices. As previously suggested (Metcalfe et al, 2014;J. Salemi et al, 2018), programs should validate algorithms locally before implementing them for surveillance.…”

Section: Selected Congenital Heart Defectsmentioning

confidence: 99%

“…Algorithm accuracy is determined in comparison to a data source that is considered to be the reference standard (Chubak, Pocobelli, & Weiss, 2012). Several studies have reported on measures of accuracy for specific CA case definition algorithms (Eltonsy et al, 2017;J. Salemi et al, 2018;Metcalfe, Sibbald, Lowry, Tough, & Bernier, 2014).…”

Section: Introductionmentioning

confidence: 99%

Validation of case definition algorithms for the ascertainment of congenital anomalies

Escalante

Abayomi

Langlois

et al. 2022

Birth Defects Research

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Background Congenital anomalies (CA) are one of the leading causes of infant mortality and long‐term disability. Many jurisdictions rely on health administrative data to monitor these conditions. Case definition algorithms can be used to monitor CA; however, validation of these algorithms is needed to understand the strengths and limitations of the data. This study aimed to validate case definition algorithms used in a CA surveillance system in British Columbia (BC), Canada. Methods A cohort of births between March 2000 and April 2002 in BC was linked to the Health Status Registry (HSR) and the BC Congenital Anomalies Surveillance System (BCCASS) to identify cases and non‐cases of specific anomalies within each surveillance system. Measures of algorithm performance were calculated for each CA using the HSR as the reference standard. Agreement between both databases was calculated using kappa coefficient. The modified Standards for Reporting Diagnostic Accuracy guidelines were used to enhance the quality of the study. Results Measures of algorithm performance varied by condition. Positive predictive value (PPV) ranged between approximately 73%–100%. Sensitivity was lower than PPV for most conditions. Internal congenital anomalies or conditions not easily identifiable at birth had the lowest sensitivity. Specificity and negative predictive value exceeded 99% for all algorithms. Conclusion Case definition algorithms may be used to monitor CA at the population level. Accuracy of algorithms is higher for conditions that are easily identified at birth. Jurisdictions with similar administrative data may benefit from using validated case definitions for CA surveillance as this facilitates cross‐jurisdictional comparison.

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Identifying Algorithms to Improve the Accuracy of Unverified Diagnosis Codes for Birth Defects

Cited by 14 publications

References 19 publications

Validity of diagnosis and procedure codes for identifying neural tube defects in infants

Validity of diagnosis and procedure codes for identifying neural tube defects in infants

Exploring classification of birth defects severity in national hospital discharge databases compared to an active surveillance program

Validation of case definition algorithms for the ascertainment of congenital anomalies

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