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.

Anatomic and physiologic classification of adults with congenital heart disease to predict adverse outcomes: Use of administrative codes compared to clinical staging.

BACKGROUND: The 2018 anatomic physiologic (AP) classification American Heart Association/American College of Cardiology (AHA/ACC) Guidelines for Adults with Congenital Heart Disease (ACHD) encompasses both native and post-operative anatomy and physiology to guide care management. As some physiologic conditions and post-operative states lack specific International Classification of Diseases (ICD) 9- Clinical Modification (CM) and 10-CM codes, an ICD code-based classification approximating the ACHD AP classification is needed for population-based studies. METHODS: A total of 232 individuals, aged ≥ 18 years at the time of a health encounter between January 1, 2010 and December 31, 2019 and identified with at least one of 87 ICD codes for a congenital heart defect were validated through medical chart review. Individuals were assigned one of 4 mutually exclusive modified AP classification categories: (1) severe AB, (2) severe CD, (3) non-severe AB, or (4) non-severe CD, based on native anatomy "severe" or "non-severe" and physiology AB ("none" or "mild") or CD ("moderate" or "severe") by two methods: (1) medical record review, and (2) ICD and Current Procedural Terminology (CPT) code-based classification. The composite outcome was defined as a combination of a death, emergency department (ED) visits, or any hospitalizations that occurred at least 6 months after the index date and was assessed by each modified AP classification method. RESULTS: Of 232 cases (52.2% male, 71.1% White), 28.4% experienced a composite outcome a median of 1.6 years after the index encounter. No difference in prediction of the composite outcome was seen based on modified AP classification between chart review and ICD code-based methodology. CONCLUSION: Modified AP classification by chart review and ICD codes are comparable in predicting the composite outcome at least 6 months after classification. Modified AP classification using ICD code-based classification of CHD native anatomy and physiology is an important tool for population-based ACHD surveillance using administrative data.

A machine learning model for predicting congenital heart defects from administrative data.

INTRODUCTION: International Classification of Diseases (ICD) codes recorded in administrative data are often used to identify congenital heart defects (CHD). However, these codes may inaccurately identify true positive (TP) CHD individuals. CHD surveillance could be strengthened by accurate CHD identification in administrative records using machine learning (ML) algorithms. METHODS: To identify features relevant to accurate CHD identification, traditional ML models were applied to a validated dataset of 779 patients; encounter level data, including ICD-9-CM and CPT codes, from 2011 to 2013 at four US sites were utilized. Five-fold cross-validation determined overlapping important features that best predicted TP CHD individuals. Median values and 95% confidence intervals (CIs) of area under the receiver operating curve, positive predictive value (PPV), negative predictive value, sensitivity, specificity, and F1-score were compared across four ML models: Logistic Regression, Gaussian Naive Bayes, Random Forest, and eXtreme Gradient Boosting (XGBoost). RESULTS: Baseline PPV was 76.5% from expert clinician validation of ICD-9-CM CHD-related codes. Feature selection for ML decreased 7138 features to 10 that best predicted TP CHD cases. During training and testing, XGBoost performed the best in median accuracy (F1-score) and PPV, 0.84 (95% CI: 0.76, 0.91) and 0.94 (95% CI: 0.91, 0.96), respectively. When applied to the entire dataset, XGBoost revealed a median PPV of 0.94 (95% CI: 0.94, 0.95). CONCLUSIONS: Applying ML algorithms improved the accuracy of identifying TP CHD cases in comparison to ICD codes alone. Use of this technique to identify CHD cases would improve generalizability of results obtained from large datasets to the CHD patient population, enhancing public health surveillance efforts.

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.

Health Care Usage Among Adolescents With Congenital Heart Defects at 5 Sites in the United States, 2011 to 2013.

Background We sought to characterize health care usage for adolescents with congenital heart defects (CHDs) using population-based multisite surveillance data. Methods and Results Adolescents aged 11 to 18 years with ≥1 CHD-related diagnosis code and residing in 5 US sites were identified in clinical and administrative data sources for the years 2011 to 2013. Sites linked data on all inpatient, emergency department (ED), and outpatient visits. Multivariable log-binomial regression models including age, sex, unweighted Charlson comorbidity index, CHD severity, cardiology visits, and insurance status, were used to identify associations with inpatient, ED, and outpatient visits. Of 9626 eligible adolescents, 26.4% (n=2543) had severe CHDs and 21.4% had Charlson comorbidity index >0. At least 1 inpatient, ED, or outpatient visit was reported for 21%, 25%, and 96% of cases, respectively. Cardiology visits, cardiac imaging, cardiac procedures, and vascular procedures were reported for 38%, 73%, 10%, and 5% of cases, respectively. Inpatient, ED, and outpatient visits were consistently higher for adolescents with severe CHDs compared with nonsevere CHDs. Adolescents with severe and nonsevere CHDs had higher health care usage compared with the 2011 to 2013 general adolescent US population. Adolescents with severe CHDs versus nonsevere CHDs were twice as likely to have at least 1 inpatient visit when Charlson comorbidity index was low (Charlson comorbidity index =0). Adolescents with CHDs and public insurance, compared with private insurance, were more likely to have inpatient (adjusted prevalence ratio, 1.5 [95% CI, 1.3-1.7]) and ED (adjusted prevalence ratio, 1.6 [95% CI, 1.4-1.7]) visits. Conclusions High resource usage by adolescents with CHDs indicates a substantial burden of disease, especially with public insurance, severe CHDs, and more comorbidities.

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.

Predicting 30-day readmission after congenital heart surgery across the lifespan.

INTRODUCTION: Hospital readmission is an important driver of costs among patients with CHD. We assessed predictors of 30-day rehospitalisation following cardiac surgery in CHD patients across the lifespan. METHODS: This was a retrospective analysis of 981 patients with CHD who had cardiac surgery between January 2011 and December 2012. A multivariate logistic regression model was used to identify demographic, clinical, and surgical predictors of 30-day readmission. Receiver operating curves derived from multivariate logistic modelling were utilised to discriminate between patients who were readmitted and not-readmitted at 30 days. Model goodness of fit was assessed using the Hosmer-Lemeshow test statistic. RESULTS: Readmission in the 30 days following congenital heart surgery is common (14.0%). Among 981 patients risk factors associated with increased odds of 30-day readmission after congenital heart surgery through multivariate analysis included a history of previous cardiac surgery (p < 0.001), longer post-operative length of stay (p < 0.001), as well as nutritional (p < 0.001), haematologic (p < 0.02), and endocrine (p = 0.04) co-morbidities. Patients who underwent septal defect repair had reduced odds of readmission (p < 0.001), as did children (p = 0.04) and adult (p = 0.005) patients relative to neonates. CONCLUSION: Risk factors for readmission include a history of cardiac surgery, longer length of stay, and co-morbid conditions. This information may serve to guide efforts to prevent readmission and inform resource allocation in the transition of care to the outpatient setting. This study also demonstrated the feasibility of linking a national subspecialty registry to a clinical and administrative data repository to follow longitudinal outcomes of interest.

Characteristics of Adults With Congenital Heart Defects in the United States.

BACKGROUND: In the United States, >1 million adults are living with congenital heart defects (CHDs), but gaps exist in understanding the health care needs of this growing population. OBJECTIVES: This study assessed the demographics, comorbidities, and health care use of adults ages 20 to 64 years with CHDs. METHODS: Adults with International Classification of Disease-9th Revision-Clinical Modification CHD-coded health care encounters between January 1, 2008 (January 1, 2009 for Massachusetts) and December 31, 2010 were identified from multiple data sources at 3 U.S. sites: Emory University (EU) in Atlanta, Georgia (5 counties), Massachusetts Department of Public Health (statewide), and New York State Department of Health (11 counties). Demographics, insurance type, comorbidities, and encounter data were collected. CHDs were categorized as severe or not severe, excluding cases with isolated atrial septal defect and/or patent foramen ovale. RESULTS: CHD severity and comorbidities varied across sites, with up to 20% of adults having severe CHD and >50% having ≥1 additional cardiovascular comorbidity. Most adults had ≥1 outpatient encounters (80% EU, 90% Massachusetts, and 53% New York). Insurance type differed across sites, with Massachusetts having a large proportion of Medicaid (75%) and EU and New York having large proportions of private insurance (44% EU, 67% New York). Estimated proportions of adults with CHD-coded health care encounters varied greatly by location, with 1.2 (EU), 10 (Massachusetts), and 0.6 (New York) per 1,000 adults based on 2010 census data. CONCLUSIONS: This was the first surveillance effort of adults with CHD-coded inpatient and outpatient health care encounters in 3 U.S. geographic locations using both administrative and clinical data sources. This information will provide a clearer understanding of health care use in this growing population.

Health Care Transition Perceptions Among Parents of Adolescents with Congenital Heart Defects in Georgia and New York.

With increasing survival trends for children and adolescents with congenital heart defects (CHD), there is a growing need to focus on transition from pediatric to adult specialty cardiac care. To better understand parental perspectives on the transition process, a survey was distributed to 451 parents of adolescents with CHD who had recent contact with the healthcare system in Georgia (GA) and New York (NY). Among respondents, 90.7% reported excellent, very good or good health-related quality of life (HRQoL) for their adolescent. While the majority of parents (77.8%) had been told by a provider about their adolescent's need to transition to adult specialty cardiac care, most reported concerns about transitioning to adult care. Parents were most commonly concerned with replacing the strong relationship with pediatric providers (60.7%), locating an appropriate adult provider (48.7%), and accessing adult health insurance coverage (43.6%). These findings may offer insights into transition planning for adolescents with CHD.

Assessing Pregnancy, Gestational Complications, and Co-morbidities in Women With Congenital Heart Defects (Data from ICD-9-CM Codes in 3 US Surveillance Sites).

Improved treatment of congenital heart defects (CHDs) has resulted in women with CHDs living to childbearing age. However, no US population-based systems exist to estimate pregnancy frequency or complications among women with CHDs. Cases were identified in multiple data sources from 3 surveillance sites: Emory University (EU) whose catchment area included 5 metropolitan Atlanta counties; Massachusetts Department of Public Health (MA) whose catchment area was statewide; and New York State Department of Health (NY) whose catchment area included 11 counties. Cases were categorized into one of 5 mutually exclusive CHD severity groups collapsed to severe versus not severe; specific ICD-9-CM codes were used to capture pregnancy, gestational complications, and nongestational co-morbidities in women, age 11 to 50 years, with a CHD-related ICD-9-CM code. Pregnancy, CHD severity, demographics, gestational complications, co-morbidities, and insurance status were evaluated. ICD-9-CM codes identified 26,655 women with CHDs, of whom 5,672 (21.3%, range: 12.8% in NY to 22.5% in MA) had codes indicating a pregnancy. Over 3 years, age-adjusted proportion pregnancy rates among women with severe CHDs ranged from 10.0% to 24.6%, and 14.2% to 21.7% for women with nonsevere CHDs. Pregnant women with CHDs of any severity, compared with nonpregnant women with CHDs, reported more noncardiovascular co-morbidities. Insurance type varied by site and pregnancy status. These US population-based, multisite estimates of pregnancy among women with CHD indicate a substantial number of women with CHDs may be experiencing pregnancy and complications. In conclusion, given the growing adult population with CHDs, reproductive health of women with CHD is an important public health issue.

Lost in the system? Transfer to adult congenital heart disease care-Challenges and solutions.

OBJECTIVE: Transfer of congenital heart disease care from the pediatric to adult setting has been identified as a priority and is associated with better outcomes. Our objective is to determine what percentage of patients with congenital heart disease transferred to adult congenital cardiac care. DESIGN: A retrospective cohort study. SETTING: Referrals to a tertiary referral center for adult congenital heart disease patients from its pediatric referral base. PATIENTS: This resulted in 1514 patients age 16-30, seen at least once in three pediatric Georgia health care systems during 2008-2010. INTERVENTIONS: We analyzed for protective factors associated with age-appropriate care, including distance from referral center, age, timing of transfer, gender, severity of adult congenital heart disease, and comorbidities. OUTCOME MEASURES: We analyzed initial care by age among patients under pediatric care from 2008 to 2010 and if patients under pediatric care subsequently transferred to an adult congenital cardiologist in this separate pediatric and adult health system during 2008-2015. RESULTS: Among 1514 initial patients (39% severe complexity), 24% were beyond the recommended transfer age of 21 years. Overall, only 12.1% transferred care to the referral affiliated adult hospital. 90% of these adults that successfully transferred were seen by an adult congenital cardiologist, with an average of 33.9 months between last pediatric visit and first adult visit. Distance to referral center contributed to delayed transfer to adult care. Those with severe congenital heart disease were more likely to transfer (18.7% vs 6.2% for not severe). CONCLUSION: Patients with severe disease are more likely to transfer to adult congenital heart disease care than nonsevere disease. Most congenital heart disease patients do not transfer to adult congenital cardiology care with distance to referral center being a contributing factor. Both pediatric and adult care providers need to understand and address barriers in order to improve successful transfer.

Surveillance of Congenital Heart Defects among Adolescents at Three U.S. Sites.

The prevalence, co-morbidities, and healthcare utilization in adolescents with congenital heart defects (CHDs) is not well understood. Adolescents (11 to 19 years old) with a healthcare encounter between January 1, 2008 (January 1, 2009 for MA) and December 31, 2010 with a CHD diagnosis code were identified from multiple administrative data sources compiled at 3 US sites: Emory University, Atlanta, Georgia (EU); Massachusetts Department of Public Health (MA); and New York State Department of Health (NY). The estimated prevalence for any CHD was 4.77 (EU), 17.29 (MA), and 4.22 (NY) and for severe CHDs was 1.34 (EU), 3.04 (MA), and 0.88 (NY) per 1,000 adolescents. Private or commercial insurance was the most common insurance type for EU and NY, and Medicaid for MA. Inpatient encounters were more frequent in severe CHDs. Cardiac co-morbidities included rhythm and conduction disorders at 20% (EU), 46% (MA), and 9% (NY) as well as heart failure at 3% (EU), 15% (MA), and 2% (NY). Leading noncardiac co-morbidities were respiratory/pulmonary (22% EU, 34% MA, 16% NY), infectious disease (17% EU, 22% MA, 20% NY), non-CHD birth defects (12% EU, 23% MA, 14% NY), gastrointestinal (10% EU, 28% MA, 13% NY), musculoskeletal (10% EU, 32% MA, 11% NY), and mental health (9% EU, 30% MA, 11% NY). In conclusion, this study used a novel approach of uniform CHD definition and variable selection across administrative data sources in 3 sites for the first population-based CHD surveillance of adolescents in the United States. High resource utilization and co-morbidities illustrate ongoing significant burden of disease in this vulnerable 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.

Estimates of adolescent and adult congenital heart defect prevalence in metropolitan Atlanta, 2010, using capture-recapture applied to administrative records.

PURPOSE: Although congenital heart defects (CHD) are one of the most common types of birth defects in the United States, subnational prevalence estimates beyond early childhood are limited. METHODS: We used capture-recapture methodology and logistic regression to estimate CHD prevalence per 1000 residents as of January 1, 2010, separately for adolescents and adults treated and living within five metropolitan Atlanta, Georgia counties, during 2008-2010. RESULTS: Data sources differed by age. Adolescents (n = 1621, aged 11-20 years) and adults (n = 3176, aged 21-64 years) were captured from at least one source. We estimated CHD prevalence to be 7.85 per 1000 adolescents (estimated n = 3718 [95% CI: 3471-4004]) and 6.08 per 1000 adults (estimated n = 12,969 [95% CI: 13,873-18,915]). When we included persons found in age-inappropriate sources, prevalence estimates increased to 11 per 1000 adolescents and 6.5 per 1000 adults. CONCLUSIONS: This method for obtaining subnational prevalence estimates provided reasonable prevalence results and identified needs for service improvement. Only one half of adolescents and one-quarter of adults with CHD were in health care within a 3-year time frame, suggesting need for better access to health insurance, transition care, and an increased number of physicians specializing in CHD care.

Population-based surveillance of congenital heart defects among adolescents and adults: surveillance methodology.

BACKGROUND: Improved treatment of congenital heart defects (CHDs) has increased survival of persons with CHDs; however, no U.S. population-based systems exist to assess prevalence, healthcare utilization, or longer-term outcomes among adolescents and adults with CHDs. METHODS: Novel approaches identified individuals aged 11-64 years who received healthcare with ICD-9-CM codes for CHDs at three sites: Emory University in Atlanta, Georgia (EU), Massachusetts Department of Public Health (MA), New York State Department of Health (NY) between January 1, 2008 (2009 for MA) and December 31, 2010. Case-finding sources included outpatient clinics; Medicaid and other claims data; and hospital inpatient, outpatient, and emergency visit data. Supplemental information came from state vital records (EU, MA), and birth defects registries (EU, NY). Demographics and diagnostic and procedural codes were linked, de-duplicated, and shared in a de-identified dataset. Cases were categorized into one of five mutually exclusive CHD severity groups; non-cardiac comorbidity codes were grouped into broad categories. RESULTS: 73,112 individuals with CHD codes in healthcare encounters were identified. Primary data source type varied: clinics (EU, NY for adolescents), claims (MA), hospital (NY for adults). There was a high rate of missing data for some variables and data varied in format and quality. Some diagnostic codes had poor specificity for CHD ascertainment. CONCLUSIONS: To our knowledge, this is the first population-based, multi-site CHD surveillance among adolescents and adults in the U.S. Identification of people living with CHDs through healthcare encounters using multiple data sources was feasible, though data quality varied and linkage/de-duplication was labor-intensive.

The 745.5 issue in code-based, adult congenital heart disease population studies: Relevance to current and future ICD-9-CM and ICD-10-CM studies.

OBJECTIVE: Although the ICD-9-CM code 745.5 is widely used to indicate the presence of a secundum atrial septal defect (ASD), it is also used for patent foramen ovale (PFO) which is a normal variant and for "rule-out" congenital heart disease (CHD). The ICD-10-CM code Q21.1 perpetuates this issue. The objective of this study was to assess whether code 745.5 in isolation or in combination with unspecified CHD codes 746.9 or 746.89 miscodes for CHD, and if true CHD positives decrease with age. DESIGN: Echocardiograms of patients with an ICD-9-CM code of 745.5 in isolation or in combination with unspecified CHD codes 746.9 or 746.89 were reviewed to validate the true incidence of an ASD. This observational, cross-sectional record review included patients between 11 and 64 years of age. RESULTS: Medical charts and echocardiograms of 190 patients (47.9% males) were reviewed. The number of falsely coded patients with 745.5 (no ASD) was high (76.3%). Forty-five (23.7%) patients had a true ASD. Among the 145 patients without an ASD, 100 (52.6%) were classified as having a PFO, 37 (19.5%) had a normal non-CHD echocardiogram, and 8 (4.2%) had some other CHD anomaly. The likelihood that 745.5 coded for a true ASD was higher in children aged 11-20 (64.3%) than adults aged 21-64 years (20.6%). CONCLUSIONS: This validation study demonstrates that 745.5 performed poorly across all ages. As 745.5 is widely used in population-level investigations and ICD-10-CM perpetuates the problem, future analyses utilizing CHD codes should consider separate analysis of those identified only through code 745.5.

The effects of maternal weight and age on pre-eclampsia and eclampsia in Haiti.

OBJECTIVE: Patient data from Maison de Naissance (MN), a rural maternity clinic in Haiti, were analyzed to determine the prevalence of pregnancy-related hypertensive disorders and the extent to which maternal weight and age are associated with these disorders in the MN population. METHODS: A case-control study design was used with cases defined as pregnant women who were presented at MN with pregnancy-related hypertensive disorders (pregnancy-induced hypertension, pre-eclampsia or eclampsia) and controls defined as those women who delivered babies at MN and were not diagnosed with a pregnancy-related hypertensive disorder. The final cohort size was 622 controls and 67 cases. Odds ratios were calculated using multivariate logistic regression. RESULTS: The incidence of pre-eclampsia and eclampsia was 7.0%. Older maternal age at delivery (OR = 3.18; 95%CI: 1.31, 7.76) and higher maternal weight (OR = 3.24; 95%CI: 1.76, 5.98) measured during prenatal care were significantly associated with pregnancy-related hypertensive disorders. Prenatal care was not significantly associated with reduced risk of pre-eclampsia/eclampsia. CONCLUSIONS: The prevalence of pregnancy-related hypertensive disorders was high relative to rates in other developing countries. More is required to reduce the rate of pre-eclampsia perhaps by targeting older and women with high weight for preconception and more intensive prenatal care.

Screening Mammography in a Public Hospital Serving Predominantly African-American Women: A Stage-Survival-Cost Model.

BACKGROUND: Ethnic and socioeconomic disparities pervade breast cancer patterns and outcomes. Mammography guidelines reflect the difficulty in optimizing mortality reduction and cost-effectiveness, with controversy still surrounding the 2009 U.S. Preventive Services Task Force (USPSTF) recommendations. This study simulates USPSTF and American Cancer Society (ACS) guidelines' effects on stage, survival, and cost of treatment in an urban public hospital. METHODS: Charts of 274 women diagnosed with stage I, II, or III breast cancer (2008-2010) were reviewed. Published tumor doubling times were used to predict size at diagnosis under simulated screening guidelines. Stage distributions under ACS and USPSTF guidelines were compared with those observed. Cohort survival for observed and hypothetical scenarios was estimated using national statistics. Treatment costs by stage, calculated from Georgia Medicaid claims data, were similarly applied. RESULTS: Mean age at diagnosis was 56 years. African Americans predominated (82.5%), with 96% publically insured or uninsured. Simulated stages at diagnosis significantly favored ACS guidelines (43.1% stage 1/38.3% stage 2/9.9% stage 3 vs. USPSTF 23.0%/53.3 %/15.0%), as did 5-year survival and cost of treatment relative to both observed and USPSTF-predicted schema (p<.0001). Following USPSTF guidelines predicted lower survival and additional costs. CONCLUSIONS: Following ACS guidelines seems to lead to earlier diagnosis for low-income African-American women and increase 5-year survival with lower overall and breast-specific costs. The data suggest that adjusting screening practices for lower socioeconomic status, ethnic minority women may prove essential in addressing cancer disparities.