Comparison of Weighting Methods to Understand Improved Outcomes Attributable to Public Health Nursing Interventions.

BACKGROUND: The complex work of public health nurses (PHNs) specifically related to mental health assessment, intervention, and outcomes, makes it difficult to quantify and evaluate the improvement in client outcomes attributable to their interventions. OBJECTIVES: We examined heterogeneity across parents of infants served by PHNs receiving different interventions; compared the ability of traditional propensity scoring methods versus energy balancing weight techniques to adjust for the complex and stark differences in baseline characteristics among those receiving different interventions; and evaluated the causal effects of the quantity and variety of PHN interventions on client health and social outcomes. METHODS: This retrospective study of 4,109 clients used existing Omaha System data generated during the routine documentation of PHN home visit data. We estimated the effects of intervention by computing and comparing weighted averages of the outcomes within the different treatment groups using two weighting methods: (a) inverse probability of treatment (propensity score) weighting and (b) energy balancing weights (EBWs). RESULTS: Clients served by PHNs differed in baseline characteristics with clients with more signs/symptoms. Both weighting methods reduced heterogeneity in the sample. EBWs were more effective than inverse probability of treatment weighting in adjusting for multifaceted confounding and resulted in close balance of 105 baseline characteristics. Weighting the sample changed outcome patterns, especially when using energy-balancing weights. Clients who received more PHN interventions and a wider variety of them had improved knowledge, behavior, and status outcomes with no plateau over time, whereas the unweighted sample showed plateaus in outcomes over the course of home visiting services. DISCUSSION: Causal analysis of PHN-generated data demonstrated PHN intervention effectiveness for clients with mental health signs/symptoms. EBWs are a promising tool for evaluating the true causal effect of PHN home-visiting interventions.

Nurse Information Security Policy Compliance, Information Competence, and Information Security Attitudes Predict Information Security Behavior.

Nurses' attitudes toward information security can influence the hospital's information resources management and development. This study investigated the relationships between nurses' information security policy compliance, information competence, and information security attitudes, which are factors that influence information security behavior. Data were collected during September 2020. The participants were 200 clinical nurses from a general hospital in Korea. The self-reported questionnaire included questions on nurses' general characteristics, information security policy compliance, information competence, and information security attitudes. Information security policy compliance ( r = 0.554, P < .001) and information competence ( r = 0.614, P < .001) were positively associated with information security attitudes. Predictors of nurses' information security attitudes were information competence ( ß = .439), information security policy compliance ( ß = .343), prior information security-related education ( ß = .113), and job position (nurse manager; ß = .101). Implications for practice include the need for strategies to develop information security policy compliance and information competence to improve information security behavior, including different approaches tailored to nurses' job positions and previous information security education.

Public health nursing workforce and learning needs: A national sample survey analysis.

OBJECTIVES: Generate national estimates of the public health nursing workforce's (1) demographic and work characteristics and (2) continuing education learning needs in the United States. DESIGN: Secondary data analysis of the 2018 National Sample Survey of Registered Nurses. SAMPLE: Total 7352 of the 50,273 survey respondents were categorized as public health nurses (PHNs), representing an estimated 467,271 national workforce. MEASUREMENTS: Survey items for demographics, practice setting, training topics, and language(s) spoken fluently were analyzed. RESULTS: Workforce demographic characteristics are included. Mental health training was the most frequently endorsed topic by PHNs, followed by patient-centered care and evidence-based care. Training topic needs vary by practice setting. CONCLUSIONS: Results here can be used as a needs assessment for national public health nursing professional development and education initiatives. Further research is needed to refine and survey a nationally representative sample in a manner meaningful to public health nursing practice.

Using data visualization to characterize whole-person health of public health nurses.

OBJECTIVE: To characterize patterns in whole-person health of public health nurses (PHNs). DESIGN AND SAMPLE: Survey of a convenience sample of PHNs (n = 132) in 2022. PHNs self-identified as female (96.2%), white (86.4%), between the ages 25-44 (54.5%) and 45-64 (40.2%), had bachelor's degrees (65.9%) and incomes of $50-75,000 (30.3%) and $75-100,000/year (29.5%). MEASUREMENTS: Simplified Omaha System Terms (SOST) within the MyStrengths+MyHealth assessment of whole-person health (strengths, challenges, and needs) across Environmental, Psychosocial, Physiological, and Health-related Behaviors domains. RESULTS: PHNs had more strengths than challenges; and more challenges than needs. Four patterns were discovered: (1) inverse relationship between strengths and challenges/needs; (2) Many strengths; (3) High needs in Income; (4) Fewest strengths in Sleeping, Emotions, Nutrition, and Exercise. PHNs with Income as a strength (n = 79) had more strengths (t = 5.570, p < .001); fewer challenges (t = -5.270, p < .001) and needs (t = -3.659, p < .001) compared to others (n = 53). CONCLUSIONS: PHNs had many strengths compared to previous research with other samples, despite concerning patterns of challenges and needs. Most PHN whole-person health patterns aligned with previous literature. Further research is needed to validate and extend these findings toward improving PHN health.

Critical consciousness of public health nurses: A descriptive, comparative survey.

BACKGROUND: Public Health Nurses (PHN) caring for vulnerable populations amid systemic inequality must navigate complex situations, and consequently they may experience serious moral distress known to be detrimental to PHN wellbeing. OBJECTIVE: Given PHN awareness of social inequities, the study aimed to determine if PHNs were motivated to enact social change and engage in social and political action to address inequality. DESIGN AND SAMPLE: A survey of 173 PHNs was conducted in fall 2022. The convenience sample was mainly female (96.5%), White (85%), had associate/bachelor's degrees (71.7%), and worked in governmental public health settings (70.7%). MEASURE: The study employed the Short Critical Consciousness Scales' subscales: Critical Reflection, Critical Motivation, and Critical Action. RESULTS: PHNs were highly motivated to address inequities (Critical Motivation = 20.83; SD = 3.16), with similarly high awareness (Critical Reflection = 17.89; SD = 5.18). However, social and political action scores were much lower (Critical Action = 7.13; SD = 2.63). A subgroup of PHNs with strong agreement regarding the impact of poverty were more likely to be younger (p = .039) and work in a community setting (p = .003); with higher scores across subscales (p < .001). CONCLUSIONS: High critical reflection and motivation among PHNs aligned with literature. Lower Critical Action scores warrant investigation into validity for PHNs, and possible role constraints.

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.

Public Health Nurse Tailored Home Visiting and Parenting Behavior for Families at Risk for Referral to Child Welfare Services, Colorado: 2018-2019.

Objectives. To examine public health nurse (PHN) intervention tailoring through the Colorado Nurse Support Program (NSP). Our 2 specific aims were to describe the NSP program and its outcomes and to determine the effects of modifying interventions on short- and long-term outcomes among NSP clients. Methods. In our retrospective causal investigation of 150 families in Colorado in 2018-2019, intervention effects were modeled via longitudinal modified treatment policy analyses. Results. Families served by PHNs improved in terms of knowledge, behavior, and status outcomes after receiving multidimensional, tailored home visiting interventions. Case management interventions provided in the first month of PHN home visits had lasting effects on behavior outcomes, and 2 additional case management interventions in the first month were estimated to have even more of an impact. Conclusions. Modern causal inference methods and real-world PHN data revealed a nuanced, fine-grained understanding of the real impact of tailored PHN interventions. Public Health Implications PHN programs such as the NSP and use of the Omaha System should be supported and extended to advance evaluations of intervention effectiveness and knowledge discovery and improve population health. (Am J Public Health. 2022;112(S3):S306-S313. https://doi.org/10.2105/AJPH.2022.306792).

Using data visualization to detect patterns in whole-person health data.

Data visualization techniques are useful for examining large multidimensional data sets. In this exploratory data analysis (EDA) study, we applied a visualization pattern detection and testing process to deidentified data to discover patterns in whole-person health for adults 65 and older. Whole-person health examines a person's environmental, psychosocial, and physical health, as well as their health-related behaviors; and assesses their strengths, challenges, and needs. Strengths are defined as assets and capabilities in the face of short-or long-term stressors. We collected data using a mobile application that delivers a comprehensive whole-person assessment using a simplified version of a standardized instrument, the Omaha System. The visualization pattern detection process is iterative, includes various techniques, and requires visualization literacy. The data visualization techniques applied in this analysis included bubble charts, parallel coordinates line graphs, box plots, and alluvial flow diagrams. We discovered six patterns within the visualizations. We formulated and tested six hypotheses based on these six patterns, and all six hypotheses were supported. Adults 65 and older had more strengths than challenges and more challenges than needs (p < 0.001). Strengths and challenges were negatively correlated (p < 0.001). Unexpectedly, a subset of adults 65 and older who had many, but not all, strengths had significantly more needs (p = 0.04). The use of standardized terminology with its inherent data interrelationships was key to discovering patterns in whole-person health. This methodology may be used in future EDA research using new data sets.

Associations between weather-related data and influenza reports: A pilot study and related policy implications.

AIM: The purpose of this retrospective, correlational pilot study was to explore the relationship between historical weekly weather data including temperature, dew point, humidity, barometric pressure, visibility, and cloud cover compared to weekly influenza-like illness reports over a four year period. BACKGROUND: Climate and weather-related conditions may affect the viral activity and transmission of influenza, although this relationship has not been widely studied in nursing. Some research suggests that there are causal links between cold temperatures, low indoor humidity, minimal sun exposure, and influenza outbreaks. Additionally, rapid weather variability in a warming climate can increase influenza epidemic risk. METHODS: Data from a local public health district were extracted and used to correlate with weekly weather averages for the area. RESULTS: Findings showed that current influenza reports are significantly associated with temperature and visibility, both lagged two weeks. CONCLUSIONS: Though more research is needed, nurses must understand, recognize, and act upon weather and climate factors that affect the health of populations. With a greater understanding of the relationship between weather and influenza-like illness, nurses and other healthcare providers can potentially work to respond to and mitigate the consequences of weather-related illness as well as anticipate and prepare for increased flu burden. Furthermore, nurses can remain engaged in climate protective initiatives and policy development at their local community and/or organizational levels to underscore and advocate for the needs of populations and groups they serve.

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

Existing frailty and social and behavioral determinants of health (SBDH) algorithms were refined and used to examine SBDH and frailty groups, revealing patterns in interventions and outcomes of older adults in a large community-based care data-set. The dataset was randomly split into training (n = 2,881) and testing (n = 1,441) sets. The training set was used to visually identify patterns in associations among SBDH, frailty, intervention doses, and outcomes, and the testing set was used to validate the patterns. Seven valid patterns were identified, showing increases in SBDH and frailty were associated with poorer health outcomes and more interventions (all p < 0.01). Findings suggest that the refined SBDH and frailty algorithms facilitate the identification of older adults with SBDH and frailty for intervention tailoring. [Journal of Gerontological Nursing, 48(4), 41-48.].

Exploring Large Community- and Clinically-Generated Datasets to Understand Resilience Before and During the COVID-19 Pandemic.

PURPOSE: To explore resilience in the context of whole-person health and the social determinants of health at the individual and community levels using large, standardized nursing datasets. DESIGN: A retrospective, observational, correlational study of existing deidentified Health Insurance Portability and Accountability Act (HIPAA)-compliant data using the Omaha System and its equivalent, Simplified Omaha System Terms. METHODS: We used three samples to explore for patterns of resilience: pre-COVID-19 community-generated data (N = 383), pre-COVID-19 clinical documentation data (N = 50,509), and during-COVID-19 community-generated data (N = 102). Community participants used the My Strengths + My Health (MSMH) app to generate the two community datasets. The clinical data were obtained from the Omaha System Data Collaborative. We operationalized resilience as Omaha System Status scores of 4 (minimal signs or symptoms) or 5 (no signs or symptoms) as a discrete strengths measure for each of 42 Omaha System problem concepts. We used visualization techniques and standard descriptive and inferential statistics for analysis. FINDINGS: It was feasible to examine resilience, operationalized as strengths by problem concept, within existing Omaha System or Simplified Omaha System Terms (MSMH) data. We identified several patterns indicating strengths and resilience that were consistent with literature related to community connectedness for community participants, and sleep for individuals in the clinical data. CONCLUSIONS: When used consistently, the Omaha System within MSMH enabled robust data collection for a comprehensive, holistic assessment, resulting in better whole-person data including strengths, and enabled us to discover a potentially useful approach for defining resilience in new ways using standardized nursing data. CLINICAL RELEVANCE: The notion that how we assess individuals and communities (i.e., the completeness of our assessments in relation to whole-person health) determines what we can know about resilience is seemingly in opposition to the critical need to decrease documentation burden, despite the potential to shift from a problem deficit-based assessment to one of strengths and resilience. However, a patient-facing comprehensive assessment that includes resilience and the social determinants of health can provide a transformative, whole-person platform for strengths-based care and population management.

Understanding Women's Cardiovascular Health Using MyStrengths+MyHealth: A Patient-Generated Data Visualization Study of Strengths, Challenges, and Needs Differences.

PURPOSE: The purpose of this data visualization study was to identify patterns in patient-generated health data (PGHD) of women with and without Circulation signs or symptoms. Specific aims were to (a) visualize and interpret relationships among strengths, challenges, and needs of women with and without Circulation signs or symptoms; (b) generate hypotheses based on these patterns; and (c) test hypotheses generated in Aim 2. DESIGN: The design of this visualization study was retrospective, observational, case controlled, and exploratory. METHODS: We used existing de-identified PGHD from a mobile health application, MyStrengths+MyHealth (N = 383). From the data, women identified with Circulation signs or symptoms (n = 80) were matched to an equal number of women without Circulation signs or symptoms. Data were analyzed using data visualization techniques and descriptive and inferential statistics. FINDINGS: Based on the patterns, we generated nine hypotheses, of which four were supported. Visualization and interpretation of relationships revealed that women without Circulation signs or symptoms compared to women with Circulation signs or symptoms had more strengths, challenges, and needs-specifically, strengths in connecting; challenges in emotions, vision, and health care; and needs related to info and guidance. CONCLUSIONS: This study suggests that visualization of whole-person health including strengths, challenges, and needs enabled detection and testing of new health patterns. Some findings were unexpected, and perspectives of the patient would not have been detected without PGHD, which should be valued and sought. Such data may support improved clinical interactions as well as policies for standardization of PGHD as sharable and comparable data across clinical and community settings. CLINICAL RELEVANCE: Standardization of patient-generated whole-person health data enabled clinically relevant research that included the patients' perspective.

Incorporating a Whole-Person Perspective in Consumer-Generated Data: Social Determinants, Resilience, and Hidden Patterns.

Given the complex health and social needs of older adults, the rapid growth of the aging population, and the increasing use of information technology in healthcare, there is a critical need for informatics solutions that advance gerontological nursing care and knowledge discovery. This article illustrates the value of standardized data for healthcare quality improvement throughout the life cycle of data capture and reuse. One such informatics solution is the MyStrengths+MyHealth app, which incorporates a whole-person perspective through the Simplified Omaha System Terms assessment, including the social and behavioral determinants of health, as well as resilience. The data describe whole-person health of older adults from MyStrengths+MyHealth for use in clinical encounters and as raw data for research. There is potential to use such standardized data to improve gerontological nursing care at the bedside and for population health management and research.

Evaluating Systemized Nomenclature of Medicine Clinical Terms Coverage of Complementary and Integrative Health Therapy Approaches Used Within Integrative Nursing, Health, and Medicine.

The use of complementary and integrative health therapy strategies for a wide variety of health conditions is increasing and is rapidly becoming mainstream. However, little is known about how or if complementary and integrative health therapies are represented in the EHR. Standardized terminologies provide an organizing structure for health information that enable EHR representation and support shareable and comparable data; which may contribute to increased understanding of which therapies are being used for whom and for what purposes. Use of standardized terminologies is recommended for interoperable clinical data to support sharable, comparable data to enable the use of complementary and integrative health therapies and to enable research on outcomes. In this study, complementary and integrative health therapy terms were extracted from multiple sources and organized using the National Center for Complementary and Integrative Health and former National Center for Complementary and Alternative Medicine classification structures. A total of 1209 complementary and integrative health therapy terms were extracted. After removing duplicates, the final term list was generated via expert consensus. The final list included 578 terms, and these terms were mapped to Systemized Nomenclature of Medicine Clinical Terms. Of the 578, approximately half (48.1%) were found within Systemized Nomenclature of Medicine Clinical Terms. Levels of specificity of terms differed between National Center for Complementary and Integrative Health and National Center for Complementary and Alternative Medicine classification structures and Systemized Nomenclature of Medicine Clinical Terms. Future studies should focus on the terms not mapped to Systemized Nomenclature of Medicine Clinical Terms (51.9%), to formally submit terms for inclusion in Systemized Nomenclature of Medicine Clinical Terms, toward leveraging the data generated by use of these terms to determine associations among treatments and outcomes.

Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation.

BACKGROUND: Public health nurses (PHNs) engage in home visiting services and documentation of care services for at-risk clients. To increase efficiency and decrease documentation burden, it would be useful for PHNs to identify critical data elements most associated with patient care priorities and outcomes. Machine learning techniques can aid in retrospective identification of critical data elements. OBJECTIVE: We used two different machine learning feature selection techniques of minimum redundancy-maximum relevance (mRMR) and LASSO (least absolute shrinkage and selection operator) and elastic net regularized generalized linear model (glmnet in R). METHODS: We demonstrated application of these techniques on the Omaha System database of 205 data elements (features) with a cohort of 756 family home visiting clients who received at least one visit from PHNs in a local Midwest public health agency. A dichotomous maternal risk index served as the outcome for feature selection. APPLICATION: Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero, and generalized linear model applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero, and generalized linear model applied on them achieved the highest accuracy of 95.5% on a held-out test set. DISCUSSION: Feature selection techniques show promise toward reducing public health nursing documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing PHNs' focus on client-specific and targeted interventions in the delivery of care.

Developing Structured Omaha System Goals for Use in an Electronic Health Record.

Use of standardized terminology has been essential for clear, concise, and accurate documentation of client assessments, care plans, and outcomes. The purpose of this study was to create standardized language goals for a case management system that used the Omaha System. A group of nursing informaticists analyzed, refined, and developed revised goals evaluated using medical vocabulary properties. A set of unique goals aligned with the Omaha System was developed with specifically designed characteristics and functionality that allowed individualization and evaluation of goal attainment. Goal statements and ratings were standardized and written to reflect goals a client could attain. The Omaha System goals served as a template for nurse case managers to use in telephonic support with clients and future development of new goals and allowed the organization the ability to generate quality metrics.

Alignment of American Association of Colleges of Nursing Graduate-Level Nursing Informatics Competencies With American Medical Informatics Association Health Informatics Core Competencies.

This study yielded a map of the alignment of American Association of Colleges of Nursing Graduate-Level Nursing Informatics Competencies with American Medical Informatics Association Health Informatics Core Competencies in an effort to understand graduate-level accreditation and certification opportunities in nursing informatics. Nursing Informatics Program Directors from the American Medical Informatics Association and a health informatics expert independently mapped the American Association of Colleges of Nursing competencies to the American Medical Informatics Association Health Informatics knowledge, skills, and attitudes. The Nursing Informatics Program Directors' map connected an average of 4.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency, whereas the health informatics expert's map connected an average of 5.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency. Agreement across the two maps ranged from 14% to 60% per American Association of Colleges of Nursing competency, revealing alignment between the two groups' competencies according to knowledge, skills, and attitudes. These findings suggest that graduates of master's degree programs in nursing, especially those specializing in nursing informatics, will likely be prepared to sit for the proposed Advanced Health Informatics Certification in addition to the American Nurses Credentialing Center bachelor's-level Informatics Nursing Certification. This preliminary map sets the stage for further in-depth mapping of nursing informatics curricula with American Medical Informatics Association Core Competencies and will enable interprofessional conversations around nursing informatics specialty program accreditation, nursing workforce preparation, and nursing informatics advanced certification. Nursing informaticists should examine their need for credentials as key contributors who will address critical health informatics needs.

Understanding tailored PHN interventions and outcomes of Latina mothers.

OBJECTIVE: To examine associations of data-driven intervention approaches (IAs) with the outcomes of adolescent or adult Latina mothers with or without the Mental health problem. DESIGN AND SAMPLE: Retrospective observational study using public health nurse (PHN)-generated data for 676 Latina mothers aged 14-52. MEASURES: Mothers' age, having the Mental health problem, number of problems and interventions, and Knowledge, Behavior, and Status scores using the Omaha System. Mother-specific percentages of interventions to the total number received were calculated. Visualizations and statistical tests were used to analyze the association of IAs relating mothers' characteristics, problems, interventions, and outcomes. RESULTS: Four IAs were discovered. Sample characteristics differed significantly among IAs by age and having the Mental health problems. There was a small effect of age on outcomes (<0.10). PHNs differentially addressed problems in IA1-IA4 compared with IA2-IA3 (p < 0.001). Mothers who received IA3 had the most improvement and highest scores in Knowledge and Behavior (all p < 0.001). Mothers who received IA2 had the most improvement and highest scores in Status (both p < 0.001). CONCLUSIONS: The IAs were associated with outcomes differentially among Latina mothers, depending on multiple complex factors. These methods may be useful in understanding intervention tailoring and should be replicated with other populations and datasets.

Feasibility of describing community strengths relative to Omaha system concepts.

OBJECTIVES: The purpose was to determine the feasibility of using a standardized language, the Omaha System, to describe community-level strengths. The objectives were: (a) to evaluate the feasibility of using the Omaha System at the community level to reflect community strengths and (b) to describe preliminary results of community strengths observations across international settings. DESIGN AND SAMPLE: A descriptive qualitative design was used. The sample was a data set of 284 windshield surveys by nursing students in 5 countries: Mexico, New Zealand, Norway, Turkey, and the United States. MEASURES: An online survey included a checklist and open-ended questions on community strengths for 11 concepts of the Omaha System Problem Classification Scheme: Income, Sanitation, Residence, Neighborhood/workplace safety, Communication with community resources, Social contact, Interpersonal relationship, Spirituality, Nutrition, Substance use, and Health care supervision. Themes were derived through content analysis of responses to the open-ended questions. RESULTS: Feasibility was demonstrated: Students were able to use the Omaha System terms and collect data on strengths. Common themes were described among the five countries. CONCLUSIONS: The Omaha System appears to be useful in documenting community-level strengths. Themes and exemplar quotes provide a first step in developing operational definitions of strengths at a more granular level.