Health plan administrative records versus birth certificate records: quality of race and ethnicity information in children

<p>Abstract</p> <p>Background</p> <p>To understand racial and ethnic disparities in health care utilization and their potential underlying causes, valid information on race and ethnicity is necessary. However, the validity of pediatric race and ethnicity information in administrative records from large integrated health care systems using electronic medical records is largely unknown.</p> <p>Methods</p> <p>Information on race and ethnicity of 325,810 children born between 1998-2008 was extracted from health plan administrative records and compared to birth certificate records. Positive predictive values (PPV) were calculated for correct classification of race and ethnicity in administrative records compared to birth certificate records.</p> <p>Results</p> <p>Misclassification of ethnicity and race in administrative records occurred in 23.1% and 33.6% children, respectively; the majority due to missing ethnicity (48.3%) and race (40.9%) information. Misclassification was most common in children of minority groups. PPV for White, Black, Asian/Pacific Islander, American Indian/Alaskan Native, multiple and other was 89.3%, 86.6%, 73.8%, 18.2%, 51.8% and 1.2%, respectively. PPV for Hispanic ethnicity was 95.6%. Racial and ethnic information improved with increasing number of medical visits. Subgroup analyses comparing racial classification between non-Hispanics and Hispanics showed White, Black and Asian race was more accurate among non-Hispanics than Hispanics.</p> <p>Conclusions</p> <p>In children, race and ethnicity information from administrative records has significant limitations in accurately identifying small minority groups. These results suggest that the quality of racial information obtained from administrative records may benefit from additional supplementation by birth certificate data.</p

Factors Associated With Receipt of Preventive Dental Treatment Procedures Among Adult Patients at a Dental Training School in Wisconsin, 2001-2002

Background: Gender differences in oral health-related quality of life and the fear of dental pain in seeking and receiving preventive dental care have been recognized and documented. Preventive dental treatment procedures (PDTPs) are commonly accepted as the primary approach to prevent dental disease. Objective: We examined whether the likelihood of receiving PDTPs differed by gender in adult patients receiving dental care at a dental training institution in Milwaukee, Wisconsin.Methods: Data from the Marquette University School of Dentistry electronic patient management database for 2001 through 2002 were analyzed. Descriptive, bivariate, and multivariable analyses were performed. The preventive procedures used in the study were those coded in accordance with the American Dental Association\u27s classification system: D1110 (adult prophylaxis: professional cleaning and polishing of the teeth), D1204 (adult topical application of fluoride), D1205 (adult topical application of fluoride plus prophylaxis), and D1330 (oral hygiene instruction).Results: Of the 1563 consecutive patient records (888 women, 675 men) reviewed for the years 2001-2002, 794 individuals (51%), aged 18 to 60 years, were identified as having received PDTPs. At the bivariate level, a significant gender difference in the receipt of PDTPs was identified (423 women [48%] vs 371 men [55%]; P = 0.004). In the multivariable analyses, age, race/ethnicity, marital status, poverty level, and health insurance type (public, private, none) were significantly associated with the receipt of PDTPs (all, P \u3c 0.05), but gender was not.Conclusions: Gender differences in receiving PDTPs were not found in this dental school patient population. Receipt of PDTPs was associated with other demographic factors such as age, race/ethnicity, marital status, income level, and health insurance

Equity in the Digital Age: How Health Information Technology Can Reduce Disparities

While enormous medical and technological advancements have been made over the last century, it is only very recently that there have been similar rates of development in the field of health information technology (HIT).This report examines some of the advancements in HIT and its potential to shape the future health care experiences of consumers. Combined with better data collection, HIT offers signi?cant opportunities to improve access to care, enhance health care quality, and create targeted strategies that help promote health equity. We must also keep in mind that technology gaps exist, particularly among communities of color, immigrants, and people who do not speak English well. HIT implementation must be done in a manner that responds to the needs of all populations to make sure that it enhances access, facilitates enrollment, and improves quality in a way that does not exacerbate existing health disparities for the most marginalized and underserved

MCRAGE: Synthetic Healthcare Data for Fairness

In the field of healthcare, electronic health records (EHR) serve as crucial training data for developing machine learning models for diagnosis, treatment, and the management of healthcare resources. However, medical datasets are often imbalanced in terms of sensitive attributes such as race/ethnicity, gender, and age. Machine learning models trained on class-imbalanced EHR datasets perform significantly worse in deployment for individuals of the minority classes compared to samples from majority classes, which may lead to inequitable healthcare outcomes for minority groups. To address this challenge, we propose Minority Class Rebalancing through Augmentation by Generative modeling (MCRAGE), a novel approach to augment imbalanced datasets using samples generated by a deep generative model. The MCRAGE process involves training a Conditional Denoising Diffusion Probabilistic Model (CDDPM) capable of generating high-quality synthetic EHR samples from underrepresented classes. We use this synthetic data to augment the existing imbalanced dataset, thereby achieving a more balanced distribution across all classes, which can be used to train an unbiased machine learning model. We measure the performance of MCRAGE versus alternative approaches using Accuracy, F1 score and AUROC. We provide theoretical justification for our method in terms of recent convergence results for DDPMs with minimal assumptions.Comment: Keywords: synthetic electronic health records, conditional denoising diffusion probabilistic model, healthcare AI, tabular data, fairness, synthetic data. This paper is the result of work completed at the 2023 Emory University Department of Mathematics REU/RET program under the direction of Project Advisor Dr. Xi Yuanzhe. This work is sponsored by NSF DMS 205101

Assessing the performance of the Asian/Pacific islander identification algorithm to infer Hmong ethnicity from electronic health records in California.

OBJECTIVE:This study assesses the performance of the North American Association of Central Cancer Registries Asian/Pacific Islander Identification Algorithm (NAPIIA) to infer Hmong ethnicity. DESIGN AND SETTING:Analyses of electronic health records (EHRs) from 1 January 2011 to 1 October 2015. The NAPIIA was applied to the EHR data, and self-reported Hmong ethnicity from a questionnaire was used as the gold standard. Sensitivity, specificity, positive (PPV) and negative predictive values (NPVs) were calculated comparing the source data ethnicity inferred by the algorithm with the self-reported ethnicity from the questionnaire. PARTICIPANTS:EHRs indicating Hmong, Chinese, Vietnamese and Korean ethnicity who met the original study inclusion criteria were analysed. RESULTS:The NAPIIA had a sensitivity of 78%, a specificity of 99.9%, a PPV of 96% and an NPV of 99%. The prevalence of Hmong population in the sample was 3.9%. CONCLUSION:The high sensitivity of the NAPIIA indicates its effectiveness in detecting Hmong ethnicity. The applicability of the NAPIIA to a multitude of Asian subgroups can advance Asian health disparity research by enabling researchers to disaggregate Asian data and unmask health challenges of different Asian subgroups

How Registries Can Help Performance Measurement Improve Care

Suggests ways to better utilize databases of clinical information to evaluate care processes and outcomes and improve measurements of healthcare quality and costs, comparative clinical effectiveness research, and medical product safety surveillance

A large multi-ethnic genome-wide association study identifies novel genetic loci for intraocular pressure.

Elevated intraocular pressure&nbsp;(IOP) is a major risk factor for glaucoma, a leading cause of blindness. IOP heritability has been estimated to up to 67%, and to date only 11 IOP loci have been reported, accounting for 1.5% of IOP variability. Here, we conduct a genome-wide association study of IOP in 69,756 untreated individuals of European, Latino, Asian, and African ancestry. Multiple longitudinal IOP measurements were collected through electronic health records and, in total, 356,987 measurements were included. We identify 47 genome-wide significant IOP-associated loci (P &lt; 5 × 10-8); of the 40 novel loci, 14 replicate at Bonferroni significance in an external genome-wide association study analysis of 37,930 individuals of European and Asian descent. We further examine their effect on the risk of glaucoma within our discovery sample. Using longitudinal IOP measurements from electronic health records improves our power to identify new variants, which together explain 3.7% of IOP variation

RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning

Anonymized electronic medical records are an increasingly popular source of research data. However, these datasets often lack race and ethnicity information. This creates problems for researchers modeling human disease, as race and ethnicity are powerful confounders for many health exposures and treatment outcomes; race and ethnicity are closely linked to population-specific genetic variation. We showed that deep neural networks generate more accurate estimates for missing racial and ethnic information than competing methods (e.g., logistic regression, random forest). RIDDLE yielded significantly better classification performance across all metrics that were considered: accuracy, cross-entropy loss (error), and area under the curve for receiver operating characteristic plots (all $p < 10^{-6}$). We made specific efforts to interpret the trained neural network models to identify, quantify, and visualize medical features which are predictive of race and ethnicity. We used these characterizations of informative features to perform a systematic comparison of differential disease patterns by race and ethnicity. The fact that clinical histories are informative for imputing race and ethnicity could reflect (1) a skewed distribution of blue- and white-collar professions across racial and ethnic groups, (2) uneven accessibility and subjective importance of prophylactic health, (3) possible variation in lifestyle, such as dietary habits, and (4) differences in background genetic variation which predispose to diseases

Aligning Forces for Quality: Local Efforts to Transform American Health Care

Profiles RWJF's initiative to raise healthcare quality in targeted communities; reduce racial/ethnic disparities; and offer models for national reform through performance measurement and public reporting, quality improvement, and consumer engagement

Closing the Disparities Gap in Healthcare Quality With Performance Measurement and Public Reporting

Provides an overview of widening disparities in healthcare quality by race/ethnicity, socioeconomic status, and insurance. Discusses efforts to close the gap, including reporting quality measures and pay-for-performance, as well as challenges in data col