Racial and Ethnic Disparities in Health Care Usage and Death by Neighborhood Poverty Among Individuals With Congenital Heart Defects, 4 US Surveillance Sites, 2011 to 2013.

BACKGROUND: Socioeconomic factors may lead to a disproportionate impact on health care usage and death among individuals with congenital heart defects (CHD) by race, ethnicity, and socioeconomic factors. How neighborhood poverty affects racial and ethnic disparities in health care usage and death among individuals with CHD across the life span is not well described. METHODS AND RESULTS: Individuals aged 1 to 64 years, with at least 1 CHD-related International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code were identified from health care encounters between January 1, 2011, and December 31, 2013, from 4 US sites. Residence was classified into lower- or higher-poverty neighborhoods on the basis of zip code tabulation area from the 2014 American Community Survey 5-year estimates. Multivariable logistic regression models, adjusting for site, sex, CHD anatomic severity, and insurance-evaluated associations between race and ethnicity, and health care usage and death, stratified by neighborhood poverty. Of 31 542 individuals, 22.2% were non-Hispanic Black and 17.0% Hispanic. In high-poverty neighborhoods, non-Hispanic Black (44.4%) and Hispanic (47.7%) individuals, respectively, were more likely to be hospitalized (adjusted odds ratio [aOR], 1.2 [95% CI, 1.1-1.3]; and aOR, 1.3 [95% CI, 1.2-1.5]) and have emergency department visits (aOR, 1.3 [95% CI, 1.2-1.5] and aOR, 1.8 [95% CI, 1.5-2.0]) compared with non-Hispanic White individuals. In high poverty neighborhoods, non-Hispanic Black individuals with CHD had 1.7 times the odds of death compared with non-Hispanic White individuals in high-poverty neighborhoods (95% CI, 1.1-2.7). Racial and ethnic disparities in health care usage were similar in low-poverty neighborhoods, but disparities in death were attenuated (aOR for non-Hispanic Black, 1.2 [95% CI=0.9-1.7]). CONCLUSIONS: Racial and ethnic disparities in health care usage were found among individuals with CHD in low- and high-poverty neighborhoods, but mortality disparities were larger in high-poverty neighborhoods. Understanding individual- and community-level social determinants of health, including access to health care, may help address racial and ethnic inequities in health care usage and death among individuals with CHD.

Positive Predictive Value of International Classification of Diseases, Ninth Revision, Clinical Modification, and International Classification of Diseases, Tenth Revision, Clinical Modification, Codes for Identification of Congenital Heart Defects.

Background Administrative data permit analysis of large cohorts but rely on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), and International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes that may not reflect true congenital heart defects (CHDs). Methods and Results CHDs in 1497 cases with at least 1 encounter between January 1, 2010 and December 31, 2019 in 2 health care systems, identified by at least 1 of 87 ICD-9-CM/ICD-10-CM CHD codes were validated through medical record review for the presence of CHD and CHD native anatomy. Interobserver and intraobserver reliability averaged >95%. Positive predictive value (PPV) of ICD-9-CM/ICD-10-CM codes for CHD was 68.1% (1020/1497) overall, 94.6% (123/130) for cases identified in both health care systems, 95.8% (249/260) for severe codes, 52.6% (370/703) for shunt codes, 75.9% (243/320) for valve codes, 73.5% (119/162) for shunt and valve codes, and 75.0% (39/52) for "other CHD" (7 ICD-9-CM/ICD-10-CM codes). PPV for cases with >1 unique CHD code was 85.4% (503/589) versus 56.3% (498/884) for 1 CHD code. Of cases with secundum atrial septal defect ICD-9-CM/ICD-10-CM codes 745.5/Q21.1 in isolation, PPV was 30.9% (123/398). Patent foramen ovale was present in 66.2% (316/477) of false positives. True positives had younger mean age at first encounter with a CHD code than false positives (22.4 versus 26.3 years; P=0.0017). Conclusions CHD ICD-9-CM/ICD-10-CM codes have modest PPV and may not represent true CHD cases. PPV was improved by selecting certain features, but most true cases did not have these characteristics. The development of algorithms to improve accuracy may improve accuracy of electronic health records for CHD surveillance.

Supervised Text Classification System Detects Fontan Patients in Electronic Records With Higher Accuracy Than ICD Codes.

Background The Fontan operation is associated with significant morbidity and premature mortality. Fontan cases cannot always be identified by International Classification of Diseases (ICD) codes, making it challenging to create large Fontan patient cohorts. We sought to develop natural language processing-based machine learning models to automatically detect Fontan cases from free texts in electronic health records, and compare their performances with ICD code-based classification. Methods and Results We included free-text notes of 10 935 manually validated patients, 778 (7.1%) Fontan and 10 157 (92.9%) non-Fontan, from 2 health care systems. Using 80% of the patient data, we trained and optimized multiple machine learning models, support vector machines and 2 versions of RoBERTa (a robustly optimized transformer-based model for language understanding), for automatically identifying Fontan cases based on notes. For RoBERTa, we implemented a novel sliding window strategy to overcome its length limit. We evaluated the machine learning models and ICD code-based classification on 20% of the held-out patient data using the F1 score metric. The ICD classification model, support vector machine, and RoBERTa achieved F1 scores of 0.81 (95% CI, 0.79-0.83), 0.95 (95% CI, 0.92-0.97), and 0.89 (95% CI, 0.88-0.85) for the positive (Fontan) class, respectively. Support vector machines obtained the best performance (P<0.05), and both natural language processing models outperformed ICD code-based classification (P<0.05). The sliding window strategy improved performance over the base model (P<0.05) but did not outperform support vector machines. ICD code-based classification produced more false positives. Conclusions Natural language processing models can automatically detect Fontan patients based on clinical notes with higher accuracy than ICD codes, and the former demonstrated the possibility of further improvement.

How Well Do ICD-9-CM Codes Predict True Congenital Heart Defects? A Centers for Disease Control and Prevention-Based Multisite Validation Project.

Background The Centers for Disease Control and Prevention's Surveillance of Congenital Heart Defects Across the Lifespan project uses large clinical and administrative databases at sites throughout the United States to understand population-based congenital heart defect (CHD) epidemiology and outcomes. These individual databases are also relied upon for accurate coding of CHD to estimate population prevalence. Methods and Results This validation project assessed a sample of 774 cases from 4 surveillance sites to determine the positive predictive value (PPV) for identifying a true CHD case and classifying CHD anatomic group accurately based on 57 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Chi-square tests assessed differences in PPV by CHD severity and age. Overall, PPV was 76.36% (591/774 [95% CI, 73.20-79.31]) for all sites and all CHD-related ICD-9-CM codes. Of patients with a code for complex CHD, 89.85% (177/197 [95% CI, 84.76-93.69]) had CHD; corresponding PPV estimates were 86.73% (170/196 [95% CI, 81.17-91.15]) for shunt, 82.99% (161/194 [95% CI, 76.95-87.99]) for valve, and 44.39% (83/187 [95% CI, 84.76-93.69]) for "Other" CHD anatomic group (X2=142.16, P<0.0001). ICD-9-CM codes had higher PPVs for having CHD in the 3 younger age groups compared with those >64 years of age, (X2=4.23, P<0.0001). Conclusions While CHD ICD-9-CM codes had acceptable PPV (86.54%) (508/587 [95% CI, 83.51-89.20]) for identifying whether a patient has CHD when excluding patients with ICD-9-CM codes for "Other" CHD and code 745.5, further evaluation and algorithm development may help inform and improve accurate identification of CHD in data sets across the CHD ICD-9-CM code groups.

Individuals aged 1-64 years with documented congenital heart defects at healthcare encounters, five U.S. surveillance sites, 2011-2013.

BACKGROUND: Many individuals born with congenital heart defects (CHD) survive to adulthood. However, population estimates of CHD beyond early childhood are limited in the U.S. OBJECTIVES: To estimate the percentage of individuals aged 1-to-64 years at five U.S. sites with CHD documented at a healthcare encounter during a three-year period and describe their characteristics. METHODS: Sites conducted population-based surveillance of CHD among 1 to 10-year-olds (three sites) and 11 to 64-year-olds (all five sites) by linking healthcare data. Eligible cases resided in the population catchment areas and had one or more healthcare encounters during the surveillance period (January 1, 2011-December 31, 2013) with a CHD-related ICD-9-CM code. Site-specific population census estimates from the same age groups and time period were used to assess percentage of individuals in the catchment area with a CHD-related ICD-9-CM code documented at a healthcare encounter (hereafter referred to as CHD cases). Severe and non-severe CHD were based on an established mutually exclusive anatomic hierarchy. RESULTS: Among 42,646 CHD cases, 23.7% had severe CHD and 51.5% were male. Percentage of CHD cases among 1 to 10-year-olds, was 6.36/1,000 (range: 4.33-9.96/1,000) but varied by CHD severity [severe: 1.56/1,000 (range: 1.04-2.64/1,000); non-severe: 4.80/1,000 (range: 3.28-7.32/1,000)]. Percentage of cases across all sites in 11 to 64-year-olds was 1.47/1,000 (range: 1.02-2.18/1,000) and varied by CHD severity [severe: 0.34/1,000 (range: 0.26-0.49/1,000); non-severe: 1.13/1,000 (range: 0.76-1.69/1,000)]. Percentage of CHD cases decreased with age until 20 to 44 years and, for non-severe CHD only, increased slightly for ages 45 to 64 years. CONCLUSION: CHD cases varied by site, CHD severity, and age. These findings will inform planning for the needs of this growing population.

Short interpregnancy intervals and adverse pregnancy outcomes by maternal age in the United States.

PURPOSE: The purpose of the article was to examine the association between short interpregnancy intervals and adverse outcomes by maternal age among U.S. women. METHODS: Using publicly available natality files for 2013-2016 singleton births, we compared the risks of preterm birth, gestational diabetes, gestational hypertension, and maternal morbidity (delivery-related complications) for less than 6-month, 6 to 11-month, and 12 to 17-month to 18- to 23-month interpregnancy intervals, overall and by maternal age. Models adjusted for maternal demographics, conditions, and behaviors. RESULTS: Among 2,365,219 births, adjusted risk ratios (aRR) for preterm birth overall for intervals less than 6, 6-11, and 12-17 months were 1.62 (95% confidence interval: 1.60, 1.65), 1.16 (1.15, 1.18), and 1.03 (1.02, 1.05), respectively, compared with 18-23 months. Intervals less than 6, 6-11, and 12-17 months were significantly protective overall for gestational diabetes (aRR range: 0.89-0.98), gestational hypertension (aRR range: 0.93-0.95), and maternal morbidity (aRR range: 0.93-1.08). All aRRs attenuated or remained flat with increasing maternal age. CONCLUSION: Interpregnancy intervals less than 18 months showed different patterns of association for preterm birth compared with maternal outcomes, overall and across age. This suggests that increasing maternal age may have discordant effects on associations between short interpregnancy interval and adverse perinatal and maternal outcomes.

Sociodemographic, insurance, and risk profiles of maternal smokers post the 1990s: how can we reach them?

Declines in prenatal smoking rates have changed the composition of maternal smokers while public policy during the 1990s has likely made it more difficult to reach them. Medicaid expansions during the 1980s/early 1990s insured more women some time during pregnancy, but the 1996 welfare reform unexpectedly reduced enrollment in Medicaid by eligible pregnant women; overall, insurance coverage has declined since 2000. As the public sector struggles with fewer resources, it is important to understand the sociodemographic characteristics of prenatal smokers, their patterns of care, and nonsmoking risk behaviors. Targeting scarce dollars to certain settings or sub-populations can strengthen the infrastructure for tobacco policy change. We provide more current information on maternal smokers in 2002 based on the Pregnancy Risk Assessment Monitoring System (PRAMS) for 21 states. Data on urban/rural location, insurance coverage, access patterns, and nonsmoking risk behaviors (e.g., abuse) among low-income (<16,000) and other maternal smokers are included. Low-income maternal smokers are the working poor living in predominately urban areas with fewer health care resources than low-income nonsmokers. Over 50% of low-income maternal smokers are uninsured pre-pregnancy and use a clinic as their usual source of care. Regardless of income, smokers exhibit rates of nonsmoking risks that are two to three times those of nonsmokers and high rates of unintended pregnancy (68%) of low-income smokers. These characteristics likely call for a bundle of social support services beyond cessation for smokers to quit and remain smoke-free postpartum.