Conectar
Background
Federally Qualified Health Centers (FQHC) provide services to individuals facing systemic barriers to health equity and are disproportionately affected by adverse social determinants of health. To better align healthcare services with the needs of those individuals experiencing health inequities, it is essential to screen for and document problematic social contributors to health in electronic health records, which health systems have been mandated to document by 2026.
Objectives
The aims of this study were to 1) determine the prevalence of documented social contributors to health Z-codes among patients receiving care through Colorado nurse-led FQHCs across urban, rural, and frontier settings; and 2) estimate healthcare utilization and expenditures associated with the presence of documented social contributors to health Z-codes compared to a matched sample of patients without that Z-code documentation.
Methods
We conducted a secondary analysis of the Colorado All Payers Claim Database. Social contributor of health ICD-10 Z-codes, reflecting problematic social structural circumstances as defined by Healthy People 2030, were extracted from patients receiving care in FQHCs. Social contributor of health-related charges were computed using propensity matching to compare individuals with and without documented social contributors of health.
Results
Documentation of social contributors of health Z-codes was notably low. Housing instability was the most common Z-code documented. Chronic pulmonary disease, diabetes, and heart disease were the most prevalent comorbidities among those with identified social contributors of health. The majority of patients with social contributors of health Z-codes were insured through Medicaid and lived in rural areas. Persons with documented social contributors of health had significantly higher predicted annual medical expenditures compared to those without documentation.
Discussion
The low prevalence of social contributors of health coding aligns with previous studies and represents a missed opportunity to provide targeted interventions for populations experiencing adverse social contributors. These findings underscore the need for strategizing and implementing plans to identify and code social contributors of health, especially in facilities serving those experiencing health inequities. Improved documentation of social contributors to health can facilitate data-driven resource allocation and tailored interventions to address adverse social determinants and promote health equity.
Background
A realist approach has gained popularity in evaluation research, particularly in understanding causal explanations of how a program works (or not), the circumstances, and the observed outcomes. In qualitative inquiry, the approach has contributed to better theoretically based explanations regarding causal interactions.
Objective
The aim of this study was to discuss how we conducted a realist-informed data analysis to explore the causal interactions within qualitative data.
Methods
We demonstrated a four-step realist approach of retroductive theorizing in qualitative data analysis using a concrete example from our empirical research rooted in the critical realism philosophical stance. These steps include (a) category identification, (b) elaboration of context-mechanism-outcome configuration, (c) demi-regularities identification, and (d) generative mechanism refinement.
Results
The four-step qualitative realist data analysis underpins the causal interactions of important factors and reveals the underlying mechanisms. The steps produce comprehensive causal explanations that can be used by related parties—especially when making complex decisions that may affect wide communities.
Discussion
The core process of realist data analysis is retroductive theorizing. The four-step qualitative realist data analysis facilitates this theorizing by allowing the researcher to identify (a) patterns, (b) fluctuation of patterns, (c) mechanisms from collected data, and (d) to confirm proposed mechanisms. 
FreshRSS