Association of Disparities in Family History and Family Cancer History in the Electronic Health Record With Sex, Race, Hispanic or Latino Ethnicity, and Language Preference in 2 Large US Health Care Systems

Abstract

ImportanceClinical decision support (CDS) algorithms are increasingly being implemented in health care systems to identify patients for specialty care. However, systematic differences in missingness of electronic health record (EHR) data may lead to disparities in identification by CDS algorithms.ObjectiveTo examine the availability and comprehensiveness of cancer family history information (FHI) in patients’ EHRs by sex, race, Hispanic or Latino ethnicity, and language preference in 2 large health care systems in 2021.Design, Setting, and ParticipantsThis retrospective EHR quality improvement study used EHR data from 2 health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Participants included patients aged 25 to 60 years who had a primary care appointment in the previous 3 years. Data were collected or abstracted from the EHR from December 10, 2020, to October 31, 2021, and analyzed from June 15 to October 31, 2021.ExposuresPrior collection of cancer FHI in primary care settings.Main Outcomes and MeasuresAvailability was defined as having any FHI and any cancer FHI in the EHR and was examined at the patient level. Comprehensiveness was defined as whether a cancer family history observation in the EHR specified the type of cancer diagnosed in a family member, the relationship of the family member to the patient, and the age at onset for the family member and was examined at the observation level.ResultsAmong 144 484 patients in the UHealth system, 53.6% were women; 74.4% were non-Hispanic or non-Latino and 67.6% were White; and 83.0% had an English language preference. Among 377 621 patients in the NYULH system, 55.3% were women; 63.2% were non-Hispanic or non-Latino, and 55.3% were White; and 89.9% had an English language preference. Patients from historically medically undeserved groups—specifically, Black vs White patients (UHealth: 17.3% [95% CI, 16.1%-18.6%] vs 42.8% [95% CI, 42.5%-43.1%]; NYULH: 24.4% [95% CI, 24.0%-24.8%] vs 33.8% [95% CI, 33.6%-34.0%]), Hispanic or Latino vs non-Hispanic or non-Latino patients (UHealth: 27.2% [95% CI, 26.5%-27.8%] vs 40.2% [95% CI, 39.9%-40.5%]; NYULH: 24.4% [95% CI, 24.1%-24.7%] vs 31.6% [95% CI, 31.4%-31.8%]), Spanish-speaking vs English-speaking patients (UHealth: 18.4% [95% CI, 17.2%-19.1%] vs 40.0% [95% CI, 39.7%-40.3%]; NYULH: 15.1% [95% CI, 14.6%-15.6%] vs 31.1% [95% CI, 30.9%-31.2%), and men vs women (UHealth: 30.8% [95% CI, 30.4%-31.2%] vs 43.0% [95% CI, 42.6%-43.3%]; NYULH: 23.1% [95% CI, 22.9%-23.3%] vs 34.9% [95% CI, 34.7%-35.1%])—had significantly lower availability and comprehensiveness of cancer FHI (P < .001).Conclusions and RelevanceThese findings suggest that systematic differences in the availability and comprehensiveness of FHI in the EHR may introduce informative presence bias as inputs to CDS algorithms. The observed differences may also exacerbate disparities for medically underserved groups. System-, clinician-, and patient-level efforts are needed to improve the collection of FHI.

Expanding Family Health History to Include Family Medication History

“…It has been well-documented that FHH is under-utilized for a variety of reasons [ 42 , 43 , 44 , 45 ] and thus, suggesting collection of further information may not be feasible. In particular, evidence suggests that FHH is under-reported [ 46 , 47 , 48 , 49 , 50 ] and potentially inaccurate or incomplete [ 51 , 52 , 53 ], with patient recall, sharing, and communication impacted by multiple factors including unawareness, differences in knowledge of maternal vs. paternal family histories, patient gender, degree of relatives, and cultural factors [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ].…”

Racial and Ethnic Disparities in Co-Occurrence of Nocturnal Hypertension and Nocturnal Blood Pressure Decreases

“…Demographics and comorbidities (diabetes, coronary artery disease, myocardial infarction, heart failure, kidney disease, and hypertension) were obtained from the electronic health record (EHR), the latter via ICD-10 codes (eTable in Supplement 1). Race and ethnicity collection for the EHR, categorized based on US Office of Management and Budget standards, was through self-report or staff queries, although patient-level details were unavailable . We used guideline-based definitions of NH (mean evening systolic BP >110 mm Hg) and nondipping BP (<10% decrease in daytime-to-nighttime mean systolic BP or increase in daytime-to-nighttime mean BP).…”

Germline Testing in Patients With Breast Cancer: ASCO–Society of Surgical Oncology Guideline

“…Recent studies have also noted disparities in the collection of family history information necessary for determining testing eligibility. 95,96 A unique challenge in germline genetic testing is the unequal distribution of VUS. These ambiguous results are more frequent among racial and ethnic groups who have received less testing of a particular gene or genes, and for whom the normal range of genetic variability is less well mapped.…”