The Breast and Cervical Cancer Prevention and Treatment Act (BCCPTA) in Georgia: Women Covered and Medicaid Costs in 2003

The Breast and Cervical Cancer Prevention and Treatment Act (BCCPTA) provided states with an optional Medicaid eligibility category for uninsured women with breast and/or cervical cancers. The BCCPTA is the first and only such effort to use a population-based public health screening program, the National Breast and Cervical Cancer Early Detection Program (NBCCEDP) to provide a pathway to publicly funded health insurance for otherwise uninsured low-income women. Georgia was one of the first states to adopt the BCCPTA and was one of only twelve states that provided Medicaid eligibility to women screened by non-NBCCEDP providers. We use 2003 Georgia Medicaid claims and enrollment data to investigate the scope of the state’s BCCPTA enrollment and enrollees’ costs as well as demographic characteristics of breast and cervical cancer patients in Georgia’s BCCPTA and other Medicaid eligibility categories. Georgia’s Medicaid coverage of women with breast and/or cervical cancer under BCCPTA accounted for over one-third of all women with these cancers covered by the state in 2003 alone. Those newly eligible under BCCPTA were more likely to have breast, as opposed to cervical, cancer and to be older than those women with breast/cervical cancers enrolled in Georgia Medicaid due to low-income, pregnancy or disability status. Georgia’s Medicaid program spent over $29 million on BCCPTA enrollees in 2003 at a cost of over$12,000 per enrollee. BCCPTA enrollee costs were more similar to those for disabled women with these cancers, about $19,500, than to costs for low-income/pregnant women which equaled about$7,500. By expanding Medicaid coverage, BCCPTA can potentially bring women in at earlier stages of their cancer and provide needed coverage/treatment. Future research should examine the potential effect of BCCPTA on reduced morbidity and mortality among these low-income women

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

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

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

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, P64 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

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