The association between social needs and chronic conditions in a large, urban primary care population
Introduction
Chronic diseases are the leading cause of death and disability in the United States, as well as the leading contributor of healthcare costs (National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP), n.d.). There is growing recognition that social determinants of health are important factors that influence health outcomes, including chronic diseases and related health behaviors (National Academies of Sciences, Engineering and M, 2019). There are multiple pathways through which unmet social needs influence health outcomes. First, specific needs, such as food insecurity or poor housing quality, increase exposure to risk factors for specific diseases such as asthma and diabetes (Cockerham et al., 2017; Shaw, 2004; Seligman et al., 2010). Second, unmet needs lead to chronic stress, which reduces adherence to healthy behaviors and clinical care. Third, chronic stress has been shown to lead to ‘wear and tear’ on the body's stress response system, which worsens health outcomes and may accumulate over the life course. Finally, unmet social needs and health outcomes may be bi-directional and feed into each other, preventing individuals from accessing resources for other needs and resulting in an additive effect for multiple needs (Berkowitz et al., 2019; Gurewich et al., 2020).
However, few studies have focused on how the burden and types of needs influence specific chronic risk factors and diseases for a large, diverse population (Gottlieb et al., 2017). Instead, most studies have focused on singular needs (Seligman et al., 2010; Berkowitz et al., 2018; Pantell et al., 2013; Burgard et al., 2012), specific populations (Thompson et al., 2019; Blazer et al., 2007a; Blosnich et al., 2017), overall health status (Prather et al., 2017; Blazer et al., 2007b) or a limited number of health conditions (Berkowitz et al., 2017; Pantell et al., 2019).
With the shift towards value-based the care, the influence of unmet social needs on health outcomes has become a priority for health systems. Several health systems are routinely integrating standardized social need screening into clinical care to connect patients to resources (Fraze et al., 2016; Lee and Korba, 2017; Bachrach et al., 2014; Krieger, 2017; Adler et al., 2016; Alley et al., n.d.; Braveman and Gottlieb, 2014). As health systems implement large-scale screenings and associated data become available, there is an opportunity to leverage both screener and clinical data to understand how the overall burden of social needs, as well as how individual social needs, are associated with common chronic conditions. Analytic models that take social needs and chronic conditions into account may be able to identify types of patients who are more impacted by multiple or specific needs as well as specific needs that require prioritization. These insights could drive decisions at the practice and intervention-level regarding referrals, support services, interventions and allocation of resources. Health systems may also find it advantageous to invest in upstream factors (i.e. housing vouchers, food pantries, etc.) based on such data to optimize outcomes and decrease costs.
The objective of this study was to examine the relationship between overall social needs burden, as well as types of social needs, and specific chronic conditions to better inform quality improvement strategies.
Section snippets
Study population and social risk screening
Beginning in April 2018, Montefiore Medical Center began implementing a social needs screener at primary care sites in the Bronx and Westchester County, NY. The ten-item screener was based on the Health Leads screener but supplemented with questions from other tools addressing community-specific priority issues (see Table 1) (Health Leads, n.d.; Heller et al., 2020). These needs were selected because they are specific, actionable, and common concerns for households based on the expert opinion
Results
Table 1 includes the wording of the screening questions and the percent of adults answering affirmatively to each question. Characteristics of the screened population are presented in Table 2. Two-thirds of respondents were female (64.7%) and the median age was 51.9 years. Half (52.8%) of the sample was Hispanic and over one-third (37.2%) was non-Hispanic Black. Most respondents (80.6%) preferred English, while 17.2% preferred Spanish. One-third of respondents were insured through Medicaid and
Discussion
This study examines the relationship between self-reported social needs and selected chronic conditions measured during clinical visits. The study adds to current social determinants of health literature by examining multiple needs and chronic conditions for a large, diverse population, which provides insight into how the relationship between outcomes and individual and multiple social needs may vary. Overall, we find that there is a significant relationship between the number and types of
Limitations
This study is not without its limitations. First, the cross-sectional nature of the data prevents us from attributing causality though the stated goal of the project was not to elucidate causal relationships. While the study examines the relationship between social needs and chronic disease, it does not examine the specific mechanisms for the observed relationships. However, a previous analysis of screening data and no-show visits from the same health system identified challenges with
Conclusions
Screening for social needs in the primary care setting provides data that can be used to improve our understanding of how social needs impact health, which can inform mitigation strategies. Results indicate there is a positive and graded association between the burden of social needs and specific chronic conditions, although the association varies by condition and is strongest for behavioral risk factors and mental health. This finding suggests that mental health-related outcomes may be the
Declaration of Competing Interest
No other financial disclosures were reported by the authors of this paper and the authors declare no conflicts of interest.
Acknowledgments
The authors are grateful to those from Montefiore Medical Group who helped develop and implement the screening tool. Dr. Chambers is supported by grants from the National Heart, Lung, and Blood Institute (K01HL125466 and 1R03HL140265) and by a grant from the National Institutes of Diabetes and Digestive and Kidney Diseases (P30DK111022 and R01DK121896). Dr. Fiori is supported by grants from the Doris Duke Charitable Foundation (2018169) and the Agency for Health Care Research and Quality, HHS (
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