Hyperferritinemic sepsis, macrophage activation syndrome, and mortality in a pediatric research network: a causal inference analysis.

BACKGROUND: One of five global deaths are attributable to sepsis. Hyperferritinemic sepsis (> 500 ng/mL) is associated with increased mortality in single-center studies. Our pediatric research network's objective was to obtain rationale for designing anti-inflammatory clinical trials targeting hyperferritinemic sepsis. METHODS: We assessed differences in 32 cytokines, immune depression (low whole blood ex vivo TNF response to endotoxin) and thrombotic microangiopathy (low ADAMTS13 activity) biomarkers, seven viral DNAemias, and macrophage activation syndrome (MAS) defined by combined hepatobiliary dysfunction and disseminated intravascular coagulation, and mortality in 117 children with hyperferritinemic sepsis (ferritin level > 500 ng/mL) compared to 280 children with sepsis without hyperferritinemia. Causal inference analysis of these 41 variables, MAS, and mortality was performed. RESULTS: Mortality was increased in children with hyperferritinemic sepsis (27/117, 23% vs 16/280, 5.7%; Odds Ratio = 4.85, 95% CI [2.55-9.60]; z = 4.728; P-value < 0.0001). Hyperferritinemic sepsis had higher C-reactive protein, sCD163, IL-22, IL-18, IL-18 binding protein, MIG/CXCL9, IL-1ß, IL-6, IL-8, IL-10, IL-17a, IFN-γ, IP10/CXCL10, MCP-1/CCL2, MIP-1α, MIP-1ß, TNF, MCP-3, IL-2RA (sCD25), IL-16, M-CSF, and SCF levels; lower ADAMTS13 activity, sFasL, whole blood ex vivo TNF response to endotoxin, and TRAIL levels; more Adenovirus, BK virus, and multiple virus DNAemias; and more MAS (P-value < 0.05). Among these variables, only MCP-1/CCL2 (the monocyte chemoattractant protein), MAS, and ferritin levels were directly causally associated with mortality. MCP-1/CCL2 and hyperferritinemia showed direct causal association with depressed ex vivo whole blood TNF response to endotoxin. MCP-1/CCL2 was a mediator of MAS. MCP-1/CCL2 and MAS were mediators of hyperferritinemia. CONCLUSIONS: These findings establish hyperferritinemic sepsis as a high-risk condition characterized by increased cytokinemia, viral DNAemia, thrombotic microangiopathy, immune depression, macrophage activation syndrome, and death. The causal analysis provides rationale for designing anti-inflammatory trials that reduce macrophage activation to improve survival and enhance infection clearance in pediatric hyperferritinemic sepsis.

Early, Persistent Lymphopenia Is Associated With Prolonged Multiple Organ Failure and Mortality in Septic Children.

OBJECTIVES: Sepsis-associated immune suppression correlates with poor outcomes. Adult trials are evaluating immune support therapies. Limited data exist to support consideration of immunomodulation in pediatric sepsis. We tested the hypothesis that early, persistent lymphopenia predicts worse outcomes in pediatric severe sepsis. DESIGN: Observational cohort comparing children with severe sepsis and early, persistent lymphopenia (absolute lymphocyte count < 1,000 cells/µL on 2 d between study days 0-5) to children without. The composite outcome was prolonged multiple organ dysfunction syndrome (MODS, organ dysfunction beyond day 7) or PICU mortality. SETTING: Nine PICUs in the National Institutes of Health Collaborative Pediatric Critical Care Research Network between 2015 and 2017. PATIENTS: Children with severe sepsis and indwelling arterial and/or central venous catheters. INTERVENTIONS: Blood sampling and clinical data analysis. MEASUREMENTS AND MAIN RESULTS: Among 401 pediatric patients with severe sepsis, 152 (38%) had persistent lymphopenia. These patients were older, had higher illness severity, and were more likely to have underlying comorbidities including solid organ transplant or malignancy. Persistent lymphopenia was associated with the composite outcome prolonged MODS or PICU mortality (66/152, 43% vs 45/249, 18%; p < 0.01) and its components prolonged MODS (59/152 [39%] vs 43/249 [17%]), and PICU mortality (32/152, 21% vs 12/249, 5%; p < 0.01) versus children without. After adjusting for baseline factors at enrollment, the presence of persistent lymphopenia was associated with an odds ratio of 2.98 (95% CI [1.85-4.02]; p < 0.01) for the composite outcome. Lymphocyte count trajectories showed that patients with persistent lymphopenia generally did not recover lymphocyte counts during the study, had lower nadir whole blood tumor necrosis factor-α response to lipopolysaccharide stimulation, and higher maximal inflammatory markers (C-reactive protein and ferritin) during days 0-3 ( p < 0.01). CONCLUSIONS: Children with severe sepsis and persistent lymphopenia are at risk of prolonged MODS or PICU mortality. This evidence supports testing therapies for pediatric severe sepsis patients risk-stratified by early, persistent lymphopenia.

Medication for Opioid Use Disorder During Pregnancy - Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014-2021.

Problem: Medication for opioid use disorder (MOUD) is recommended for persons with opioid use disorder (OUD) during pregnancy. However, knowledge gaps exist about best practices for management of OUD during pregnancy and these data are needed to guide clinical care. Period Covered: 2014-2021. Description of the System: Established in 2019, the Maternal and Infant Network to Understand Outcomes Associated with Medication for Opioid Use Disorder During Pregnancy (MAT-LINK) is a surveillance network of seven clinical sites in the United States. Boston Medical Center, Kaiser Permanente Northwest, The Ohio State University, and the University of Utah were the initial clinical sites in 2019. In 2021, three clinical sites were added to the network (the University of New Mexico, the University of Rochester, and the University of South Florida). Persons receiving care at the seven clinical sites are diverse in terms of geography, urbanicity, race and ethnicity, insurance coverage, and type of MOUD received. The goal of MAT-LINK is to capture demographic and clinical information about persons with OUD during pregnancy to better understand the effect of MOUD on outcomes and, ultimately, provide information for clinical care and public health interventions for this population. MAT-LINK maintains strict confidentiality through robust information technology architecture. MAT-LINK surveillance methods, population characteristics, and evaluation findings are described in this inaugural surveillance report. This report is the first to describe the system, presenting detailed information on funding, structure, data elements, and methods as well as findings from a surveillance evaluation. The findings presented in this report are limited to selected demographic characteristics of pregnant persons overall and by MOUD treatment status. Clinical and outcome data are not included because data collection and cleaning have not been completed; initial analyses of clinical and outcome data will begin in 2023. Results: The MAT-LINK surveillance network gathered data on 5,541 reported pregnancies with a known pregnancy outcome during 2014-2021 among persons with OUD from seven clinical sites. The mean maternal age was 29.7 (SD = ±5.1) years. By race and ethnicity, 86.3% of pregnant persons were identified as White, 25.4% as Hispanic or Latino, and 5.8% as Black or African American. Among pregnant persons, 81.6% had public insurance, and 84.4% lived in urban areas. Compared with persons not receiving MOUD during pregnancy, those receiving MOUD during pregnancy were more likely to be older and White and to have public insurance. The evaluation of the surveillance system found that the initial four clinical sites were not representative of demographics of the South or Southwest regions of the United States and had low representation from certain racial and ethnic groups compared with the overall U.S. population; however, the addition of three clinical sites in 2021 made the surveillance network more representative. Automated extraction and processing improved the speed of data collection and analysis. The ability to add new clinical sites and variables demonstrated the flexibility of MAT-LINK. Interpretation: MAT-LINK is the first surveillance system to collect comprehensive, longitudinal data on pregnant person-infant dyads with perinatal outcomes associated with MOUD during pregnancy from multiple clinical sites. Analyses of clinical site data demonstrated different sociodemographic characteristics between the MOUD and non-MOUD treatment groups. Public Health Actions: MAT-LINK is a timely and flexible surveillance system with data on approximately 5,500 pregnancies. Ongoing data collection and analyses of these data will provide information to support clinical and public health guidance to improve health outcomes among pregnant persons with OUD and their children.

What is the relationship between falls, functional limitations, and depressive symptoms among Chinese older adults? The role of living alone.

OBJECTIVES: Although the association between falls and depressive symptoms is well documented, the mechanisms underlying this association remain largely unexplored. We investigated the mediation role of functional limitations in the association between falls and depressive symptoms among Chinese older adults and determined whether the living arrangement (living alone or not) is a significant moderator of the above-mentioned mediation pathway. STUDY DESIGN: Cross-sectional study. MAIN OUTCOME MEASURES: Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale short form (CESD-10), on which higher scores indicate higher levels of depressive symptoms. RESULTS: We used the harmonized China Health and Retirement Longitudinal Study national baseline data (2011-2012 year) involving 7410 participants aged 60 years and over. After adjusting for covariates (e.g., age and sex), the effects of falls on depressive symptoms were seen to be mediated by functional limitations among Chinese older adults (ß = 0.82, p < .001). The moderated mediation analysis, which assesses whether an indirect effect is conditional on values of a moderating variable, found that the mediation effect was contingent upon the living arrangement (ß = -0.60, p = .029). Specifically, the levels of functional limitations and depressive symptoms were higher for people with falls who were living with others relative to those living alone. CONCLUSIONS: These results suggest that functional limitations are an important intervening variable that links falls to depressive symptoms among Chinese older adults. Interventions to promote older adults' physical function and prevent falls are recommended to decrease the risk of depressive symptoms. These interventions can particularly benefit those who live with others.

Demonstration Project: Transitioning a Research Network to New Single IRB Platforms.

Since the 2016 National Institutes of Health (NIH) mandate to use a single IRB (sIRB) in multicenter research, institutions have struggled to operationalize the process. In this demonstration project, the University of Utah Trial Innovation Center assisted the Collaborative Pediatric Critical Care Research Network to transition from using individually negotiated reliance agreements and paper-based documentation to a new sIRB master agreement and an informatics platform to capture reliance documentation. Lessons learned that can guide other academic institutions and IRBs as they operationalize sIRBs included the need for sites to understand what type of engagement or reliance is required and their need to understand the difference between reliance and activation. Requirements around local review remain poorly understood. Further research is needed to determine approaches that can achieve the NIH vision of reviews becoming more efficient and improving study start-up times, relieving administrative burden while advancing human research protections.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Consent Builder: an innovative tool for creating research informed consent documents.

Objective: To describe process innovations related to research informed consent documents, and development and formative evaluation of Consent Builder, a platform for generating consent documents for multicenter studies. Materials and Methods: Analysis of Institutional Review Board workflows and documents, followed by process redesign, document redesign, and software development. Locally developed software leverages REDCap and LaTeX. A small-scale usability study was conducted. Results: Process innovations were combining document types, and conceptualizing 2-part informed consent documents: part 1 standardizing the study description and part 2 with local site verbiage. Consent Builder was implemented in the Trial Innovation Network. User survey scores were acceptable; but areas for improvement were noted. LaTeX coding was the biggest challenge for users. Discussion: The process changes were generally well accepted. The software implementation uncovered un-accounted for assumptions, and variability in IRB review workflow across centers. Technical modifications may be needed before widespread implementation. Conclusion: We demonstrated proof-of-concept of an approach to generate research consent documents that are consistent across sites in study description, but which allow for customization of local site verbiage. The Consent Builder tool is an example of an operational innovation, helping meet a need that arose in part due to regulations around use of Single IRB for multicenter trials.

Assessing EHR Data for Use in Clinical Improvement and Research.

ABSTRACT: Data from electronic health records (EHRs) are becoming accessible for use in clinical improvement projects and nursing research. But the data quality may not meet clinicians' and researchers' needs. EHR data, which are primarily collected to document clinical care, invariably contain errors and omissions. This article introduces nurses to the secondary analysis of EHR data, first outlining the steps in data acquisition and then describing a theory-based process for evaluating data quality and cleaning the data. This process involves methodically examining the data using six data quality dimensions-completeness, correctness, concordance, plausibility, currency, and relevance-and helps the clinician or researcher to determine whether data for each variable are fit for use. Two case studies offer examples of problems that can arise and their solutions.

A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People.

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral (n = 8), cognitive (n = 14), emotional (n = 15), interpersonal (n = 13), and somatic (n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.

Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposures.

Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.

W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.

Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.

Provider Preferences for Patient-Generated Health Data Displays in Pediatric Asthma: A Participatory Design Approach.

OBJECTIVE: There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting. METHODS: In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences. RESULTS: We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements. CONCLUSION: Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs.

BACKGROUND: Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD. OBJECTIVE: In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration. METHODS: We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers. RESULTS: We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit. CONCLUSIONS: We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.

Anemia Management in Rural Haitian Children: A Mixed Methods Study.

INTRODUCTION: We examined factors influencing anemia outcomes in rural children following implementation of a prevention program. METHOD: Mixed methods study of children, parents, and clinicians utilized statistical modeling and content/ethnographic analysis. Retrospective chart abstraction evaluated treatments administered and measured hemoglobin in children aged 6 to 59 months (n = 161). Prospective interviews/questionnaires examined parent (n = 51) and clinician (n = 19) perceptions. RESULTS: Anemia prevalence decreased by 21.2%. Predictors of increased hemoglobin were clinic visit number and age at first visit. Once anemia improved, children were likely to remain improved (P = .65). Despite favorable program perceptions, stakeholders emphasized ecological barriers, including social disadvantage and local practices. DISCUSSION: Socioeconomic factors prevented guideline concordant behaviors. Persistent attention to intrapersonal, interpersonal, and community social determinants is a sine qua non for successfully managing the epidemic. The first step to provide culturally congruent care is to explicitly acknowledge that guideline-concordant behaviors are often complex.

Predicting Falls Among Community-Dwelling Older Adults: A Demonstration of Applied Machine Learning.

Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity of publicly available population-level datasets, identification of factors that predict clinical outcomes is possible using machine learning algorithms. This study demonstrates how to apply a machine learning approach to nursing-relevant questions, specifically an approach to predict falls among community-dwelling older adults, based on data from the 2014 Behavioral Risk Factor Surveillance System. A random forest algorithm, a common approach to machine learning, was compared to a logistic regression model. Explanations of how to interpret the models and their associated performance characteristics are included to serve as a tutorial to readers. Machine learning methods constitute an increasingly important approach for nursing as population-level data are increasingly being made available to the public.

Loneliness as a mediator of the impact of social isolation on cognitive functioning of Chinese older adults.

BACKGROUND AND OBJECTIVE: older adults have increased risk of social isolation, loneliness and cognitive functioning impairment, but the relationships among these factors are not conclusive. We investigated the potential mediation mechanism of loneliness on the association between social isolation and cognitive functioning among Chinese older adults within their cultural context. DESIGN: secondary analysis of the baseline wave (2011-12) of the harmonised China Health and Retirement Longitudinal Study. SETTING AND SUBJECTS: community-dwelling older adults in China (N = 7,410 participants aged 60-101 years). METHODS: we applied a multiple indicator multiple cause approach to determine whether the construct of social isolation is well defined by four indicators (social activity engagement, weekly adult children contact, caregiving for grandchildren and living alone) and used structural equation modelling to examine the direct and indirect effects among variables of interest. RESULTS: the results demonstrated that social activity engagement, weekly adult children contact and caregiving for grandchildren were significantly related to social isolation (ß = -0.26 to -0.28) (Living alone was fixed to 1 for model identification.) The indirect effect of social isolation on cognitive functioning through loneliness was significant (ß = -0.15), indicating loneliness was an important mediator. However, the direct effect of social isolation on cognitive functioning also remained significant (ß = -0.83), suggesting a partial mediation effect. CONCLUSIONS: our study highlights the mediation role of loneliness in the relationship between social isolation and cognitive functioning among Chinese older adults. The findings support the beneficial effects of maintaining social relations and coping with feelings of loneliness on older adults' cognitive functioning.

Multi-morbidity and Highly Effective Contraception in Reproductive-Age Women in the US Intermountain West: a Retrospective Cohort Study.

BACKGROUND: Women with chronic health conditions benefit from reproductive planning and access to highly effective contraception. OBJECTIVE: To determine the prevalence of and relationship between chronic health conditions and use of highly effective contraception among reproductive-age women. DESIGN: Retrospective cohort study using electronic health records. PARTICIPANTS: We identified all women 16-49 years who accessed care in the two largest health systems in the US Intermountain West between January 2010 and December 2014. MAIN MEASURES: We employed administrative codes to identify highly effective contraception and flag chronic health conditions listed in the US Medical Eligibility Criteria for Contraceptive Use (US MEC) and known to increase risk of adverse pregnancy outcomes. We described use of highly effective contraception by demographics and chronic conditions. We used multinomial logistic regression to relate demographic and disease status to contraceptive use. KEY RESULTS: Of 741,612 women assessed, 32.4% had at least one chronic health condition and 7.3% had two or more chronic conditions. Overall, 7.6% of women with a chronic health condition used highly effective contraception vs. 5.1% of women without a chronic condition. Women with chronic conditions were more likely to rely on public health insurance. The proportion of women using long-acting reversible contraception did not increase with chronic condition number (5.8% with 1 condition vs. 3.2% with 5 or more). In regression models adjusted for age, race, ethnicity, and payer, women with chronic conditions were more likely than those without chronic conditions to use highly effective contraception (aRR 1.4; 95% CI 1.4-1.5). Public insurance coverage was associated with both use of long-acting reversible contraception (aRR 2.2; 95% CI 2.1-2.3) and permanent contraception (aRR 2.9; 95% CI 2.7-3.1). CONCLUSIONS: Nearly a third of reproductive-age women in a regional health system have one or more chronic health condition. Public insurance increases the likelihood that women with a chronic health condition use highly effective contraception.

Patient-generated health data and electronic health record integration: a scoping review.

OBJECTIVES: Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs). METHODS: In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed. RESULTS: A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review. DISCUSSION: PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.