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BackgroundDespite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support. ObjectiveThis study aims to translate a novel algorithm for earlier, more accurate prediction of rapid lung function decline in patients with CF into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians. MethodsLongitudinal clinical history, lung function measurements, and time-invariant characteristics were obtained for 30,879 patients with CF who were followed in the US Cystic Fibrosis Foundation Patient Registry (2003-2015). We iteratively developed the application using the R Shiny framework and by conducting a qualitative study with care provider focus groups (N=17). ResultsA clinical conceptual model and 4 themes were identified through coded feedback from application users: (1) ambiguity in rapid decline, (2) clinical utility, (3) clinical significance, and (4) specific suggested revisions. These themes were used to revise our application to the currently released version, available online for exploration. This study has advanced the application’s potential prognostic utility for monitoring individuals with CF lung disease. Further application development will incorporate additional clinical characteristics requested by the users and also a more modular layout that can be useful for care provider and family interactions. ConclusionsOur framework for creating an interactive and visual analytics platform enables generalized development of applications to synthesize, model, and translate electronic health data, thereby enhancing clinical decision support and improving care and health outcomes for chronic diseases and disorders. A prospective implementation study is necessary to evaluate this tool’s effectiveness regarding increased communication, enhanced shared decision-making, and improved clinical outcomes for patients with CF.

One in 10 people die awaiting transplantation from donor shortage. Only half of Americans register as organ donors. In this cross-sectional study, we evaluated population-level associations of neighborhood socioeconomic deprivation and racial segregation on organ donor registration rates. We analyzed state identification card demographic and organ donor registration data from 5 states to estimate the association between a neighborhood socioeconomic deprivation index (range [0, 1]; higher values indicate more deprivation) and a racial index of concentration at the extreme (ICE) (range [-1, 1]; lower values indicate predominantly black neighborhoods, higher values indicate predominantly white neighborhoods) on organ donor registration rates within a specified geography (census tract or ZIP code tabulation area [ZCTA]). Among 26 720 738 registrants, 32% of the sample were registered organ donors. At the census tract level, with each 0.1 decrease in the deprivation index, the organ donor registration rate increased by 6.8% (95% confidence interval [CI]: 6.6%, 7.0%). With each 0.1 increase in the racial ICE, the rate increased by 1.5% (95% CI: 1.5%, 1.6%). These associations held true at the ZCTA level. Areas with less socioeconomic deprivation and a higher concentration of white residents have higher organ donor registration rates. Public health initiatives should consider neighborhood context and novel data sources in designing optimal intervention strategies.

BackgroundNeighborhood socioeconomic deprivation is associated with adverse health outcomes. We sought to determine if neighborhood socioeconomic deprivation was associated with adherence to immunosuppressive medications after liver transplantation.MethodsWe conducted a secondary analysis of a multicenter, prospective cohort of children enrolled in the medication adherence in children who had a liver transplant study (enrollment 2010-2013). Participants (N = 271) received a liver transplant ≥1 year before enrollment and were subsequently treated with tacrolimus. The primary exposure, connected to geocoded participant home addresses, was a neighborhood socioeconomic deprivation index (range 0-1, higher indicates more deprivation). The primary outcome was the medication level variability index (MLVI), a surrogate measure of adherence to immunosuppression in pediatric liver transplant recipients. Higher MLVI indicates worse adherence behavior; values ≥2.5 are predictive of late allograft rejection.ResultsThere was a 5% increase in MLVI for each 0.1 increase in deprivation index (95% confidence interval, -1% to 11%; P = 0.08). Roughly 24% of participants from the most deprived quartile had an MLVI ≥2.5 compared with 12% in the remaining 3 quartiles (P = 0.018). Black children were more likely to have high MLVI even after adjusting for deprivation (adjusted odds ratio 4.0 95% confidence interval, 1.7-10.6).ConclusionsThis is the first study to evaluate associations between neighborhood socioeconomic deprivation and an objective surrogate measure of medication adherence in children posttransplant. These findings suggest that neighborhood context may be an important consideration when assessing adherence. Differential rates of medication adherence may partly explain links between neighborhood factors and adverse health outcomes following pediatric liver transplantation.

Introduction Report-back of chemical exposure data to participants in biomonitoring studies has been shown to be beneficial to participants and researchers, and guidebooks are available regarding best practices to develop [...]

Rationale: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. Objectives: To determine whether the racist policy of redlining in the 1930s led to present-day disparities in childhood asthma by increasing community-level poverty and decreasing neighborhood socioeconomic position (SEP). Methods: We categorized census tracts at the birth address of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into categories A, B, C, and D as defined by the Home Owners Loan Corporation, with D being the highest perceived risk. Surrogates of present-day neighborhood-level SEP were determined for each tract, including the percentage of low-income households, the CDC's Social Vulnerability Index, and other tract-level variables. We performed causal mediation analysis, which, under the assumption of no unmeasured confounding, estimates the direct and mediated pathways by which redlining may cause asthma disparities through tract-level mediators adjusting for individual-level covariates. Measurements and Main Results: Of 4,849 children, the cumulative incidence of asthma through age 11 was 26.6%, and 13.2% resided in census tracts with a Home Owners Loan Corporation grade of D. In mediation analyses, residing in Grade-D tracts (adjusted odds ratio = 1.03 [95% confidence interval = 1.01, 1.05]) was significantly associated with childhood asthma, with 79% of this increased risk mediated by percentage of low-income households; results were similar for the Social Vulnerability Index and other tract-level variables. Conclusions: The historical structural racist policy of redlining led to present-day asthma disparities in part through decreased neighborhood SEP. Policies aimed at reversing the effects of structural racism should be considered to create more just, equitable, and healthy communities. [ABSTRACT FROM AUTHOR]

Objectives We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. Materials and Methods We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. Results We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. Discussion Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. Conclusion We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions. [ABSTRACT FROM AUTHOR]

Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma.

Background and objectives: Population-wide racial inequities in child health outcomes are well documented. Less is known about causal pathways linking inequities and social, economic, and environmental exposures. Here, we sought to estimate the total inequities in population-level hospitalization rates and determine how much is mediated by place-based exposures and community characteristics. Methods: We employed a population-wide, neighborhood-level study that included youth <18 years hospitalized between July 1, 2016 and June 30, 2022. We defined a causal directed acyclic graph a priori to estimate the mediating pathways by which marginalized population composition causes census tract-level hospitalization rates. We used negative binomial regression models to estimate hospitalization rate inequities and how much of these inequities were mediated indirectly through place-based social, economic, and environmental exposures. Results: We analyzed 50 719 hospitalizations experienced by 28 390 patients. We calculated census tract-level hospitalization rates per 1000 children, which ranged from 10.9 to 143.0 (median 45.1; interquartile range 34.5 to 60.1) across included tracts. For every 10% increase in the marginalized population, the tract-level hospitalization rate increased by 6.2% (95% confidence interval: 4.5 to 8.0). After adjustment for tract-level community material deprivation, crime risk, English usage, housing tenure, family composition, hospital access, greenspace, traffic-related air pollution, and housing conditions, no inequity remained (0.2%, 95% confidence interval: -2.2 to 2.7). Results differed when considering subsets of asthma, type 1 diabetes, sickle cell anemia, and psychiatric disorders. Conclusions: Our findings provide additional evidence supporting structural racism as a significant root cause of inequities in child health outcomes, including outcomes at the population level. [ABSTRACT FROM AUTHOR]