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
Despite experiencing an increased incidence in stroke, minority groups have the lowest reported knowledge of stroke signs and symptoms and are less likely to receive time-sensitive interventions. There is a need to explore the relationship between social determinants of health and stroke awareness and severity within at-risk communities.
Objectives
The aim of this study was to explore relationships between social determinants of health and stroke awareness and severity in an underserved area located in a city borough in Northeastern United States.
Methods
We used an exploratory descriptive retrospective design and existing data. Data from 1,162 patients discharged with a principal diagnosis of stroke (ischemic stroke, subarachnoid hemorrhage, or intracerebral hemorrhage) were analyzed to explore the relationship between social determinants of health (age, sex, race, ethnicity, payer status, zip code) and stroke awareness and severity.
Results
Age group, race, and hospital arrival method were all significant indicators of time to arrival at the hospital. When holding all other factors in the model constant, being 65 years of age or older was associated with a decrease in time to reach the hospital compared to those under 65 years of age. Those who traveled by private transport had longer discovery to arrival times when holding all other factors constant, as compared to those who arrived by emergency medical services. Compared to persons who were White, persons who were Black or Asian saw increased times to hospital arrival.
Discussion
This study found that social determinants of health including age, race, sex, and insurance type significantly influenced stroke awareness and/or severity. These study findings can be used by stroke program leadership and community nurses to direct community-centered stroke awareness outreach. Background
Chronic pain is a major cause of distress and disability, and biomarkers may aid in the assessment and treatment of it. Urine metabolites may be valuable bioindicators that can provide biological insight regarding chronic pain.
Objectives
To investigate the relationship between a multimarker composite measure of metabolites and patient-reported outcomes scores in adults with chronic pain, using data from a pragmatic clinical trial with a sequential, multiple-assignment randomized trial design.
Methods
Self-reported measures and urine samples from 169 active-duty service members with chronic pain were collected. Urine was analyzed using a preestablished panel of metabolites, including four previously identified biomarkers of pain: kynurenic acid, pyroglutamic acid, ethylmalonic acid, and methylmalonate. Multivariable linear regression models—adjusted for participant characteristics such as age and sex—were used to cross-sectionally examine the relationship between 11 patient-reported outcomes (fatigue, sleep-related impairment, anxiety, depression, anger, pain catastrophizing, physical function, pain interference, satisfaction with participation with social roles, pain intensity, and pain impact score) and the four urine metabolites both individually and as a composite (urine metabolite pain indicator, or UMPI). Given the study’s small sample size and exploratory nature, a significance threshold of p ≤ .10 was used for all analyses.
Results
The UMPI showed statistically significant associations with five self-reported measures (fatigue, anxiety, depression, physical functioning, and pain impact score); adjusted Pearson correlations ranged from .18 to .25. Individual metabolite analyses supported these findings, with all relationships between individual metabolites and self-reported measures showing positive associations. Kynurenic acid and ethylmalonic acid showed the strongest associations, each having statistically significant relationships with four individual self-reported measures, while pyroglutamic acid had statistically significant relationships with three self-reported measures and methylmalonate with none. The UMPI demonstrated feasible reliability.
Discussion
Our finding of associations between the UMPI and components of the self-reported measures supports the development of the UMPI and these four urine metabolites as biomarkers for chronic pain outcomes. Further research is planned and will be essential for establishing mechanistic insight and guiding biomarker development within the context of pain management. No abstract available Obesity is health epidemic associated with health conditions specific to women’s health. Healthcare providers must identify and develop a follow-up plan for patients with a body mass index of greater than 30 kg/m2 to meet the Merit-Based Incentive Payment System Quality Program rate for body mass index screening and follow-up. Barriers to addressing obesity in this population by healthcare providers include time available for counseling and knowledge about appropriate diagnosis and treatment options. This is a quality improvement project that implements a clinical template within an existing electronic health record platform that includes a treatment order set and prepopulated counseling prompts to improve the rate of which healthcare providers address obesity within the women’s health clinic. After 12 weeks, 27 patients started a weight management plan, and the Merit-Based Incentive Payment System rate increased from 59% to 67%. Implementation of order set templates into electronic health record platforms with counseling guidance provides a framework for providers to develop a plan to address obesity to meet their patient’s health goals and reduce health disparities related to obesity in women. No abstract available Background
Tongue-tie is associated with nipple pain and early breastfeeding cessation. To date, research has been limited by small sample sizes and a dearth of evidence on the effects of tongue-tie on infant feeding symptoms and physiologic breastfeeding mechanics.
Objectives
In this article, we describe the protocol for our study exploring infant feeding, negative breastfeeding symptoms, maternal anatomy, and physiologic sucking data between infants with and without tongue-tie.
Methods
A prospective cohort study design is being employed. Over 8 weeks, three visits will be conducted with a sample of mothers and their infants without tongue-tie and a sample of mothers and their infants diagnosed with tongue-tie undergoing treatment via frenotomy. The aims of the study were to compare breastfeeding symptoms, breast anatomy, infant feeding symptoms, feeding efficiency, and nutritive sucking parameters between infants with and without tongue-tie, further comparing these metrics pretreatment and posttreatment via frenotomy with the non-tongue-tied age-matched counterparts.
Results
This study is currently ongoing.
Discussion
Tongue-tie is an everyday problem; clear guidelines are needed to decide whether to treat it. This novel, innovative, and multidisciplinary research study aims to fill critical gaps in understanding the physiological and functional effects of tongue-tie on breastfeeding, offering evidence to inform better clinical decisions and support effective interventions. This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness. The study spanned 529 days and analyzed data using 70 features. Three machine learning algorithms—random forest, k nearest neighbor, and XGBoost classifiers—were developed for this purpose. The random forest classifier was shown to be the most effective, boasting an area under the curve of 0.86, an F1 score of 0.87, and a precision of 0.84. Key findings revealed that factors such as wake-up stress, wake-up heart rate, sedentary seconds, total distance traveled, and sleep duration significantly correlated with the caregivers' sleep quality. This research highlights the potential of wearable technology in assessing and predicting sleep quality, offering a pathway to creating targeted support measures for dementia caregivers from underserved groups. The study suggests that such technology can be instrumental in enhancing the well-being of these caregivers across diverse populations. 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 (EBW) 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) 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 EBWs. 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. Background
Preterm birth affects 10% of all births annually in the United States. Outcomes of people born preterm are challenging to predict because of multiple influences, including gestational age, birth weight, and social and environmental contexts, that contribute to an individual’s growth and developmental trajectory. The influence of toxic stress is underrepresented in the literature assessing preterm birth outcomes.
Objectives
The current analyses use the eco-bio-developmental model of poverty and preterm birth as a framework to model the pathways among toxic stress, preterm birth, and neurodevelopmental outcomes. Poverty and toxic stress were hypothesized to increase the risk for impaired neurodevelopmental and academic outcomes—both directly and indirectly.
Methods
The analytic sample of 55,873 children aged 6–17 years was derived from the National Survey of Children’s Health 2020–2021. Structural equation models with combined measurement and path models for each dependent variable were constructed using latent toxic stress variables. The structural path equations included direct paths from the latent measure of prenatal toxic stress, preterm birth status, and the latent measure of childhood toxic stress to the neurodevelopmental outcome, as well as an indirect, mediated path from prenatal toxic stress through preterm birth status to the outcome.
Results
Across models, higher levels of prenatal toxic stress were significantly associated with preterm birth and lower birth weight ranges. Preterm low birth weight status was associated with a greater likelihood of neurodevelopmental impairment, repeating a grade, and special education plans. The predicted probabilities of neurodevelopmental impairment, repeating grades, and special education plans are significantly higher with above-average levels of exposure to prenatal and childhood toxic stress.
Discussion
There is a need for prospective studies that assess predictors and outcomes of preterm birth that are stratified by gestational age and consider the timing, chronicity, and influence of toxic stress and environmental exposures. There is an imperative for public health programs and policies designed to support families, caregivers, and children to address the individual and structural social determinants of health that contribute to toxic stress, thereby increasing preterm birth rates and negatively affecting the outcomes of children born preterm. Background
A healthy nursing workforce is vital to ensuring that patients are provided quality care. Assessing nurses' well-being and related factors requires routine evaluations from health system leaders that leverage brief psychometrically sound measures. To date, measures used to assess nurses' well-being have primarily been psychometrically tested among other clinicians or nurses working in specific clinical practice settings rather than in large, representative, heterogeneous samples of nurses.
Objectives
This study aimed to psychometrically test measures frequently used to evaluate factors linked to nurse well-being in a heterogeneous sample of nurses within a large academic health system.
Methods
This cross-sectional, survey-based study used a convenience sample of nurses working across acute care practice settings. A total of 177 nurses completed measures, which included the Professional Quality of Life, the short form of the Professional Quality of Life measure, the two-item Connor–Davidson Resilience Scale, the five-item World Health Organization Well-Being Index, the Secondary Traumatic Stress Scale, and the single-item Mini-Z. Internal reliability and convergent validity were assessed for each measure.
Results
All the measures were found to be reliable. Brief measures used to assess domains of well-being demonstrated validity with longer measures, as evident by significant correlation coefficients.
Discussion
This study provides support for the reliability and validity of measures commonly used to assess well-being in a diverse sample of nurses working across acute care settings. Data from routine assessments of the nursing workforce hold the potential to guide the implementation and evaluation of interventions capable of promoting workplace well-being. Assessments should include psychometrically sound, low-burden measures, such as those evaluated in this study. Episodes of decompensation are the main cause of hospital admissions in patients with heart failure. For this reason, the use of mobile apps emerges as an excellent strategy to improve coverage, real-time monitoring, and timeliness of care. ControlVit is an electronic application for early detection of complications studied within the context of a tertiary university hospital. Patients were randomized to the use of ControlVit versus placebo, during a 6-month follow-up. The primary outcome was the difference in numbers of readmissions and deaths for heart failure between both groups. One hundred forty patients were included (intervention = 71, placebo = 69), with an average age of 66 years old; 71% were men. The main etiology of heart failure was ischemic (60%), whereas the main comorbidities were arterial hypertension (44%), dyslipidemia (42%), hypothyroidism (38%), chronic kidney disease (38%), and diabetes mellitus (27%). The primary outcome occurred more frequently in the control group: readmission due to decompensation for heart failure (control group n = 14 vs intervention group n = 3; P = .0081), and death (control group n = 11 vs intervention group n = 3; P = .024). In heart failure patients, ControlVit is a useful and supplementary tool, which reduces hospital admissions due to episodes of decompensation. No abstract available The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management. Hospitals are experiencing a nursing shortage crisis that is expected to worsen over the next decade. Acute care settings, which manage the care of very complex patients, need innovations that lessen nurses' workload burden while ensuring safe patient care and outcomes. Thus, a pilot study was conducted to evaluate the feasibility of implementing a large-scale acute care telenurse program, where a hospital-employed telenurse would complete admission and discharge processes for hospitalized patients virtually. In 3 months, almost 9000 (67%) of patient admissions and discharges were conducted by an acute care telenurse, saving the bedside nurse an average of 45 minutes for each admission and discharge. Preliminary benefits to the program included more uninterrupted time with patients, more complete hospital admission and discharge documentation, and positive patient and nurse feedback about the program. Background
Women veterans have a high prevalence of traditional and nontraditional risks for cardiovascular disease (CVD) including obesity and posttraumatic stress disorder. Experts from the U.S. Department of Veterans Affairs have called for actions to improve the cardiovascular health of this population. One approach is to assess women veterans’ barriers to care-seeking for CVD prevention, to inform future intervention research.
Objective
The objective of this study was to describe women veterans’ barriers to care-seeking for CVD prevention, guided by the theory of care-seeking behavior and concept awareness.
Methods
Using a cross-sectional, descriptive design, a national sample of 245 women veterans participated in an online survey about barriers to care-seeking. Participants provided narrative responses to open-ended items, endorsements to closed-ended items, and rankings of their top five barriers. Researchers conducted poststratification weighting of numerical data to reflect the women veteran population.
Results
Narrative responses described unaffordable and inaccessible services, feeling harassed or not respected in healthcare settings, and lack of awareness of risks for CVD. Frequently endorsed barriers were unaffordable and inaccessible services. Frequently ranked barriers were feeling not respected in healthcare settings and clinicians not recommending CVD prevention.
Discussion
Findings support concepts in theory of care-seeking behavior and concept awareness. Understanding women veterans’ barriers to care-seeking for CVD prevention can inform clinicians and researchers as they address these barriers. Background
The state of New Jersey has a large Black/African American (AA) versus White racial disparity in infant mortality and educational level at childbirth. This disparity, measured by rate ratio, increases with greater maternal education among varied racial–ethnic groups. The nature of this disparity measured by rate differences has not been explored.
Objectives
Infant birth and mortality data were used to examine whether racial or ethnic disparities in infant mortality increased with greater maternal education, comparing rate differences and rate ratios. Racial and ethnic variations in the association between maternal education and infant mortality were examined.
Methods
Data were from the New Jersey State Health Assessment Data for all New Jersey births between 2014 and 2018 stratified by race and ethnicity, maternal education, and infant mortality (n = 481,333). R software was used to create a data set and estimate additive and multiplicative interactions, rate differences, and rate ratios for infant mortality by maternal race/ethnicity and educational levels among four racial–ethnic groups.
Results
Infant mortality was significantly greater for Black/AA and Hispanic mothers than for White mothers. At all educational levels, Black/AA mothers had the highest prevalence of infant mortality compared to other racial or ethnic groups. Rate differences in infant mortality showed a decrease in Black/AA–White differences for mothers with a high school education or less compared to mothers with a college degree. However, rate ratios showed an increase in Black/AA–White ratio with increasing education levels for mothers with high school education or less than mothers with a college degree. Risk ratios comparing infant mortality for Black/AA versus Hispanic or Asian mothers showed more than a twofold greater risk at all education levels for Black/AA infants. Finally, college-educated Black/AA mothers had significantly higher rates of infant mortality than White or Hispanic mothers with a high school education or less.
Discussion/Implications
Black/AA mothers with a college degree had a higher infant mortality rate than White, Hispanic, or Asian mothers with a high school education or less. Future research should address contextual/systemic contributors to this disparity. Background
Substantial effort has been invested to combat childhood obesity, but overall effects are disappointing, especially in low-income racial minority children. One possible reason is a lack of focus on the important stress–eating connection. Stress can negatively influence eating behaviors, leading to an increased appetite for high-fat and energy-dense foods.
Objective
This study aimed to evaluate the preliminary effects of a healthy eating and stress management program targeting multiple theoretical variables on improving eating behavior (dyads’ fruit/vegetable intake, emotional eating), food insecurity, anthropometric characteristics (dyads’ body mass index, % body fat), cardiovascular health (dyads’ blood pressure), and mental well-being (parental stress).
Methods
A one-group, quasi-experimental pilot study was conducted among 107 low-income parent–preschooler dyads. The 14-week program included a parent component, a parent–preschooler learning component, and a day care-based preschooler component.
Results
The program had positive effects on improving dyads’ fruit/vegetable intake, food insecurity, body mass index, and blood pressure and parents’ nutrition knowledge, self-efficacy, support, food resource management behavior, problem-focused coping, and home eating environment. The overall satisfaction rate was 95.2%, and 88.1% stated that the program assisted their families with having a healthy lifestyle.
Discussion
Results support the preliminary effects of the program on improving health outcomes in rural and urban low-income families. Although warranting further investigation with a more rigorous randomized controlled trial, the healthy eating and stress management program provides a potential solution to the current coexistence of an obesity epidemic and mental health crisis. Clinical practice nurses need adequate digital competence to use technologies appropriately at work. Questionnaires measuring clinical practice nurses' digital competence lack content validity because attitude is not included as a measure of digital competence. The aim of the current study was to identify items for an item pool of a questionnaire to measure clinical practice nurses' digital competence and to evaluate the content validity. A normative Delphi study was conducted, and the content validity index on item and scale levels was calculated. In each round, 21 to 24 panelists (medical informatics specialists, nurse informatics specialists, digital managers, and researchers) were asked to rate the items on a 4-point Likert scale ranging from “not relevant” to “very relevant.” Within three rounds, the panelists reached high consensus and rated 26 items of the initial 37 items as relevant. The average content validity index of 0.95 (SD, 0.07) demonstrates that the item pool showed high content validity. The final item pool included items to measure knowledge, skills, and attitude. The items included represent the international recommendations of core competences for clinical nursing. Future research should conduct psychometric testing for construct validity and internal consistency of the generated item pool. Background
The prevalence of hypertension is 55% among African American/Black women, who have a higher risk for poor health outcomes compared to women from other racial and ethnic groups, in part because of uncontrolled blood pressure. Previous research results suggest that peers may positively influence self-management of chronic conditions like hypertension. However, few studies have described the personal characteristics of peers in the health social networks of Black women.
Objective
This substudy aimed to examine health social networks and describe the peers’ characteristics, as reported by a convenience sample of Black women with hypertension.
Methods
In this analysis of data from a larger study, 94 Black women with hypertension attending a church conference participated in a cross-sectional, descriptive study. Their mean age was 59 years, and their mean systolic blood pressure was 143 mm Hg. All participants completed a survey to gather data about (a) the characteristics of individuals they discussed health matters with (their peers or health social network) and (b) their perceptions about hypertension status and knowledge of hypertension among the peers in their health social network.
Results
Collectively, participants from the larger study named a total of 658 peers who were part of their health social networks; the mean health social network size was six peers. The peers were mostly women, Black, family members, and, on average, 54 years old. The participants discussed hypertension with 71% of the peers, reported that 36% had hypertension, and felt that 67% were somewhat or very knowledgeable about the condition. A small, positive correlation existed between the participants’ health social network size (number of peers named) and their systolic blood pressure levels.
Discussion
The health social network peers were similar to those in the larger study, with most of the same gender, race, and age. The findings of this analysis may be used to help practitioners and scientists guide patients in building health social networks for support in self-managing hypertension and conducting future studies to examine the best strategies for developing and using health social networks to improve health outcomes and reduce health disparities. 
FreshRSS 