Collecting Race, Ethnicity, and Language Data to Identify and Reduce Health Disparities: Perceptions of Health Plan Enrollees

Abstract available at publisher's website

Responses of Massachusetts hospitals to a state mandate to collect race, ethnicity and language data from patients: a qualitative study

<p>Abstract</p> <p>Background</p> <p>A Massachusetts regulation implemented in 2007 has required all acute care hospitals to report patients' race, ethnicity and preferred language using standardized methodology based on self-reported information from patients. This study assessed implementation of the regulation and its impact on the use of race and ethnicity data in performance monitoring and quality improvement within hospitals.</p> <p>Methods</p> <p>Thematic analysis of semi-structured interviews with executives from a representative sample of 28 Massachusetts hospitals in 2009.</p> <p>Results</p> <p>The number of hospitals using race, ethnicity and language data internally beyond refining interpreter services increased substantially from 11 to 21 after the regulation. Thirteen of these hospitals were utilizing patient race and ethnicity data to identify disparities in quality performance measures for a variety of clinical processes and outcomes, while 16 had developed patient services and community outreach programs based on findings from these data. Commonly reported barriers to data utilization include small numbers within categories, insufficient resources, information system requirements, and lack of direction from the state.</p> <p>Conclusions</p> <p>The responses of Massachusetts hospitals to this new state regulation indicate that requiring the collection of race, ethnicity and language data can be an effective method to promote performance monitoring and quality improvement, thereby setting the stage for federal standards and incentive programs to eliminate racial and ethnic disparities in the quality of health care.</p

Identification of Limited English Proficient Patients in Clinical Care

BackgroundStandardized means to identify patients likely to benefit from language assistance are needed.ObjectiveTo evaluate the accuracy of the U.S. Census English proficiency question (Census-LEP) in predicting patients' ability to communicate effectively in English.DesignWe investigated the sensitivity and specificity of the Census-LEP alone or in combination with a question on preferred language for medical care for predicting patient-reported ability to discuss symptoms and understand physician recommendations in English.ParticipantsThree hundred and two patients &gt; 18 who spoke Spanish and/or English recruited from a cardiology clinic and an inpatient general medical-surgical ward in 2004-2005.ResultsOne hundred ninety-eight (66%) participants reported speaking English less than "very well" and 166 (55%) less than "well"; 157 (52%) preferred receiving their medical care in Spanish. Overall, 135 (45%) were able to discuss symptoms and 143 (48%) to understand physician recommendations in English. The Census-LEP with a high-threshold (less than "very well") had the highest sensitivity for predicting effective communication (100% Discuss; 98.7% Understand), but the lowest specificity (72.6% Discuss; 67.1% Understand). The composite measure of Census-LEP and preferred language for medical care provided a significant increase in specificity (91.9% Discuss; 83.9% Understand), with only a marginal decrease in sensitivity (99.4% Discuss; 96.7% Understand).ConclusionsUsing the Census-LEP item with a high-threshold of less than "very well" as a screening question, followed by a language preference for medical care question, is recommended for inclusive and accurate identification of patients likely to benefit from language assistance

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

Hospital Performance, the Local Economy, and the Local Workforce: Findings from a US National Longitudinal Study

Blustein and colleagues examine the associations between changes in hospital performance and their local economic resources. Locationally disadvantaged hospitals perform poorly on key indicators, raising concerns that pay-for-performance models may not reduce inequality

Quality Improvement Efforts Under Health Reform: How To Ensure That They Help Reduce Disparities--Not Increase Them

Abstract available at publisher's web site