NHSN and Social Determinants of Health

Mission

To protect patients, healthcare personnel and to promote safety, quality, and value in both national and international healthcare delivery systems.

Vision

To protect lives by leading the nation’s trusted surveillance system for healthcare.

Mission

• To use rigorous science for real-time data and surveillance to protect patients, protect healthcare personnel and to promote safety, quality, and value in healthcare.
• To lead the nation’s efforts to identify and respond to emerging and persisting threats across healthcare with best-in-class data automation and user interfaces.

Underlying racial and ethnic inequities and system barriers for other language speakers significantly affect health outcomes. However, few studies have systematically addressed these factors and their impact on hospital and long-term care facility (LTCF) acquired infections. Similarly, few studies have addressed these factors and their impact on COVID-19, influenza, and respiratory syncytial virus (RSV) vaccination uptake by healthcare workers and LTCF residents.

Race, ethnicity, language, and interpreter (REaLI) data fields already exist in most electronic medical record systems. There are rapidly evolving requirements on the horizon to collect these essential data elements. By looking at traditional race and ethnicity categories, and diving deeper into population sub-groups who speak languages other than English, more specific and actionable differences in infection risk and vaccine uptake may be identified.

In order to better understand the impacts and interactions of REaLI data on hospital and LTCF associated infections, as well as COVID-19, influenza, and RSV vaccination uptake by healthcare workers and LTCF residents, we are taking steps to require the submission of REaLI data in a systematic and standardized way in NHSN, with guidelines to support this effort.

We intend to analyze the data to identify disparities in infection burden and vaccine uptake, which can be used to inform action for hospital, LTCF, state, and local public health systems. The goal is to mitigate infection risks and vaccine uptake barriers associated with REaLI data identifiers. Recommendations may include specific sub-population outreach, including other language speakers within communities, collaboration with ethnic community-based organizations to better understand how culture, language, and structural barriers impact these risks, and specific actions to directly address health systems barriers.

Medical providers and their patients must be able to effectively and clearly communicate with each other. Lack of equitable language access for clear communication is a social determinant of health.   

Evidence demonstrates that poorer health outcomes are associated with patients who speak languages other than English (LOE) and include less access to timely COVID-19 vaccinations,^1,2 increased morbidity and mortality associated with COVID-19 infection,¹ and increased risks for central line-associated blood stream infections.³

Patients who speak LOE encounter barriers to health care that include difficulties accessing phone lines to receive nurse advice, making medical appointments, asking pharmacy questions, obtaining prescription updates, and understanding medical diagnoses, treatment plans, consent to treatment, and follow-up care.

Based on these facts, NHSN has prioritized the requirement for systematic EHR documentation of preferred language(s) spoken by patients, and the need for medical interpretation services.

With the systematic, required collection of primary language(s) spoken and interpreter need, health systems and NHSN will be able to reliably track the association of hospital- and long-term care facility (LTCF)-acquired infections with patients who speak LOE and need medical interpretation. Similarly, health systems will be able to track the impact of patients who speak LOE on COVID-19, influenza, and RSV vaccination uptake by healthcare workers and LTCF residents.  From a practical standpoint, health systems, medical interpretation service companies, and public health agencies will be able to, in real time, identify an increased need for interpreters in languages spoken in newly arriving populations and tailor responses to these new language needs.