Introduction
It is well established that social determinants influence health outcomes.1 While many definitions exist, WHO defines social determinants of health as the conditions in which we are born, grow, live, work and age.2 Research shows that social determinants, such as poverty, education and housing, have more influence on health than genes, health behaviours and medical care.3
Integrating community-level data with clinical data is an approach for identifying and addressing relevant social determinants and can lead to improved health outcomes.4 Technical advances in recent years allow for providers to assess community-level data as they pertain to individual patients in real time. Bazemore et al assigned geographical identifiers to a patient’s address and linked those identifiers to existing non-clinical data sets on the built environment, economic conditions, demographics and resources. These small area data sets, referred to as ‘Community Vital Signs,’ were then uploaded into the patient’s electronic health records (EHR).5 Liaw et al assigned patient addresses to census tracts, linked each census tract with a Social Deprivation Index (SDI) and other data on poverty and education, then identified patients living in ‘cold spots.’ Further, they found that living in a ‘cold spot’ is associated with poor clinical outcomes and lower quality of care.6
Combining multiple neighborhood-level determinants into a single measure of deprivation, as in Phillips et al, allows for multidimensional vulnerability assessment and resource targeting. By using a deprivation index, practitioners can easily view a community’s ‘vital signs’ and provide context-informed care, where care plans are tailored based on community characteristics.7 While there is no consensus on which community characteristics are most important in measuring social deprivation and predicting health outcomes, researchers have developed and tested various models. Despite these indices being developed at various geographies, including tract8 and county,9 and being composed of different indicators, they all illustrate that high levels of deprivation are associated with poor health outcomes and the need to look beyond patient characteristics when exploring clinical quality.
Given the influence of community characteristics on health outcomes, community-level social deprivation is of particularly interest to federally funded health centres. The Health Resources and Services Administration’s (HRSA) Health centre Programme serves the most vulnerable populations regardless of ability to pay. Nationwide, nearly 1400 health centre organisations serve over 28 million patients at approximately 12 000 service delivery sites.10 Clinical quality data are reported annually by health centre organisations as part of their Uniform Data System (UDS) report and are used by HRSA as part of their quality improvement efforts.11 Further, incorporating service-area-level community characteristics will help support the ongoing efforts of the federal government to include social risk factors as part of value-based payment reform.12
HRSA-funded health centres (henceforth referred to as health centres) are well positioned and are currently working to address social determinants. As health centres serve the most vulnerable patients and their communities, incorporating community-level or service-area-level determinants are just as important as clinical data when evaluating health centre quality outcomes (henceforth referred to as Clinical Quality Measures (CQMs)). To test the impact of social deprivation on health centre performance and clinical outcomes, we created a weighted service area social deprivation score for each health centre and explored clinical performance and quality by service area social deprivation quartile.