Frailty and Social and Behavioral Determinants of Health: Algorithm Refinement and Pattern Validation

Advantages and disadvantages of using theory-based versus data-driven models with social and behavioral determinants of health data

OBJECTIVE

Theory-based research of social and behavioral determinants of health (SBDH) found SBDH-related patterns in interventions and outcomes for pregnant/birthing people. The objectives of this study were to replicate the theory-based SBDH study with a new sample, and to compare these findings to a data-driven SBDH study.

MATERIALS AND METHODS

Using deidentified public health nurse-generated Omaha System data, 2 SBDH indices were computed separately to create groups based on SBDH (0-5+ signs/symptoms). The data-driven SBDH index used multiple linear regression with backward elimination to identify SBDH factors. Changes in Knowledge, Behavior, and Status (KBS) outcomes, numbers of interventions, and adjusted R-squared statistics were computed for both models.

RESULTS

There were 4109 clients ages 13-40 years. Outcome patterns aligned with the original research: KBS increased from admission to discharge with Knowledge improving the most; discharge KBS decreased as SBDH increased; and interventions increased as SBDH increased. Slopes of the data-driven model were steeper, showing clearer KBS trends for data-driven SBDH groups. The theory-based model adjusted R-squared was 0.54 (SE = 0.38) versus 0.61 (SE = 0.35) for the data-driven model with an entirely different set of SBDH factors.

CONCLUSIONS

The theory-based approach provided a framework to identity patterns and relationships and may be applied consistently across studies and populations. In contrast, the data-driven approach can provide insights based on novel patterns for a given dataset and reveal insights and relationships not predicted by existing theories. Data-driven methods may be an advantage if there is sufficiently comprehensive SBDH data upon which to create the data-driven models.

Future advancement of health care through standardized nursing terminologies: reflections from a Friends of the National Library of Medicine workshop honoring Virginia K. Saba

OBJECTIVE

To honor the legacy of nursing informatics pioneer and visionary, Dr. Virginia Saba, the Friends of the National Library of Medicine convened a group of international experts to reflect on Dr. Saba's contributions to nursing standardized nursing terminologies.

PROCESS

Experts led a day-and-a-half virtual update on nursing's sustained and rigorous efforts to develop and use valid, reliable, and computable standardized nursing terminologies over the past 5 decades. Over the course of the workshop, policymakers, industry leaders, and scholars discussed the successful use of standardized nursing terminologies, the potential for expanded use of these vetted tools to advance healthcare, and future needs and opportunities. In this article, we elaborate on this vision and key recommendations for continued and expanded adoption and use of standardized nursing terminologies across settings and systems with the goal of generating new knowledge that improves health.

CONCLUSION

Much of the promise that the original creators of standardized nursing terminologies envisioned has been achieved. Secondary analysis of clinical data using these terminologies has repeatedly demonstrated the value of nursing and nursing's data. With increased and widespread adoption, these achievements can be replicated across settings and systems.

Algorithm development to improve intervention effectiveness for parents with mental health signs and symptoms

OBJECTIVES

In this study we aimed to describe and compare groups formed by a rules-based algorithm to prospectively identify clients at risk of poor outcomes in order to guide tailored public health nursing (PHN) intervention approaches.

DESIGN

Data-driven methods using standardized Omaha System PHN documentation.

SAMPLE

Clients ages 13-40 who received PHN home visiting services for both the Caretaking/parenting and Mental health problems (N = 4109).

MEASUREMENT

We applied a theory-based algorithm consisting of six rules using existing Omaha System data. We examined the groups formed by the algorithm using standard descriptive, inferential statistics, and Latent Class Analysis.

RESULTS

Clients (N = 4109) were 25.1 (SD = 5.9) years old and had an average of 7.3 (SD = 3.2) problems, 250 (SD = 319) total interventions, and 32 (SD = 44) Mental health interventions. Overall outcomes improved after PHN interventions (p < .001 for all) and having more Mental health signs/symptoms was negatively associated with outcome scores (p < .001 for all).

CONCLUSIONS

This algorithm may be helpful in identifying high-risk clients during a baseline assessment who may benefit from more intensive mental health interventions. Findings show there is value using the Omaha System for PHN documentation and algorithm clinical decision support development. Future research should focus on algorithm implementation in PHN clinical practice.