Evaluating the type of pediatric dental care use in the context of neighborhood opportunity.

BACKGROUND: Increasing evidence supports the influence of neighborhood factors on health care use and outcomes. This study measured the association between area-level social determinants of health (SDH) and type of dental care use among Ohio pediatric Medicaid beneficiaries. METHODS: A retrospective dental claims analysis was completed for children aged 1 through 5 years enrolled in Ohio Medicaid with a dental visit in 2017. Dental care use was measured from 2017 through 2021 as 1 of 4 visit types: (1) preventive, (2) caries treatment, (3) dental general anesthesia (GA), and (4) dental emergency department. The Ohio Children's Opportunity Index defined area-level SDH at the census tract level. Exploratory analysis included descriptive statistics of area-level SDH for each outcome. Poisson regression models were developed to examine the associations between the number of each dental care use outcome and Ohio Children's Opportunity Index quintiles. Visualizations were facilitated with geospatial mapping. RESULTS: Fifty-six percent of children (10,008/17,675) had caries treatment visits. Overall area-level SDH were positively associated with preventive (fifth vs first quintile incidence rate ratio [IRR], 1.09; 95% CI, 1.07 to 1.12), caries treatment (fifth vs first quintile IRR, 1.16; 95% CI, 1.08 to 1.24), and dental GA visits (fifth vs first quintile IRR, 2.13; 95% CI, 1.13 to 4.01). CONCLUSIONS: Children with preventive, caries treatment, and dental GA visits were more likely to live in neighborhoods with better SDH. Future efforts should investigate the mechanisms by which area-level factors influence dental access and use. PRACTICAL IMPLICATIONS: Neighborhood factors influence pediatric dental care use. Patient home addresses might add value to caries risk assessment tools and efforts by care networks to optimize efficient care use.

Impact of Telemedicine on Delivery of Pediatric Inflammatory Bowel Disease Care.

OBJECTIVES: Outpatient inflammatory bowel disease (IBD) care shifted from office visits (OVs) to a model with integrated telemedicine during the 2020 COVID-19 pandemic. We describe the impact of this shift on delivery of pediatric IBD care. METHODS: We collected electronic medical record data from office and telemedicine visits for pediatric patients with IBD at a single center from April 2019 to December 2020. We compared visit volume, duration, and test ordering between 2019 and 2020, and between OV and telemedicine, and assessed for differences in telemedicine adoption by sociodemographic factors. RESULTS: Visit volume was maintained between 2019 and 2020. Median overall appointment time was shorter for telemedicine versus OV [46 (interquartile range, IQR 35-72) vs 62 (IQR 51-80) minutes; P < 0.001] with no significant difference in time spent with provider [28 (IQR 21-41) vs OV 30 (IQR 24-39) minutes; P = 0.08]. Accounting for drive time, telemedicine visits were 2.6 times shorter than office visits in 2020 ( P < 0.001). In univariate analyses, there was no difference in telemedicine utilization by race or gender. Variables significantly associated with telemedicine were older age, English as primary language, being non-Hispanic, commercial insurance, living in an area of very high opportunity, and having a longer drive time to the office ( P < 0.05 for all comparisons). In multivariate analyses, visits among patients with commercial insurance were significantly more likely to be conducted via telemedicine ( P = 0.02). Among those with a telemedicine visit, multivariate analyses demonstrated multiracial patients were significantly more likely to have video visits (vs audio-only; P = 0.02), while patients with public insurance, no or missing insurance, and whose primary language was Arabic were significantly less likely to have video visits ( P < 0.05 for all comparisons). CONCLUSIONS: Integrated telemedicine allowed for continued delivery of pediatric IBD care and significantly decreased appointment time. While telemedicine may improve access for those who live further from the office, concerns remain about the introduction of disparities.

Predicting emergency department visits among children with asthma in two academic medical systems.

Objective: To develop and validate a predictive algorithm that identifies pediatric patients at risk of asthma-related emergencies, and to test whether algorithm performance can be improved in an external site via local retraining.Methods: In a retrospective cohort at the first site, data from 26 008 patients with asthma aged 2-18 years (2012-2017) were used to develop a lasso-regularized logistic regression model predicting emergency department visits for asthma within one year of a primary care encounter, known as the Asthma Emergency Risk (AER) score. Internal validation was conducted on 8634 patient encounters from 2018. External validation of the AER score was conducted using 1313 pediatric patient encounters from a second site during 2018. The AER score components were then reweighted using logistic regression using data from the second site to improve local model performance. Prediction intervals (PI) were constructed via 10 000 bootstrapped samples.Results: At the first site, the AER score had a cross-validated area under the receiver operating characteristic curve (AUROC) of 0.768 (95% PI: 0.745-0.790) during model training and an AUROC of 0.769 in the 2018 internal validation dataset (p = 0.959). When applied without modification to the second site, the AER score had an AUROC of 0.684 (95% PI: 0.624-0.742). After local refitting, the cross-validated AUROC improved to 0.737 (95% PI: 0.676-0.794; p = 0.037 as compared to initial AUROC).Conclusions: The AER score demonstrated strong internal validity, but external validity was dependent on reweighting model components to reflect local data characteristics at the external site.

The Impact of the COVID-19 Pandemic on Pediatric Bicycle Injury.

Bicycling is a common childhood activity that is associated with significant injury risk. This study's aim was to assess pediatric bicycle injury epidemiology and impacts of the COVID-19 pandemic. We conducted a cross-sectional evaluation of patients age < 18 years presenting with bicycle injury to a pediatric trauma center. A pre-pandemic period (1 March 2015-29 February 2020) was compared to the pandemic period (1 March 2020-28 February 2021). A total of 611 injury events for children < 18 years were included (471 pre-pandemic events and 140 pandemic events). The relative frequency of pandemic injuries was greater than pre-pandemic injuries (p < 0.001), resulting in a 48% increase in pandemic period injuries versus the pre-pandemic average (141 pandemic vs. 94.4/year pre-pandemic). Individuals of female sex represented a larger proportion of injuries in the pandemic period compared to the pre-pandemic period (37% pandemic vs. 28% pre-pandemic, p = 0.035). Injuries were more common on weekends versus weekdays (p = 0.01). Time series analysis showed a summer seasonality trend. Localizing injury events to ZIP codes showed regional injury density patterns. During COVID-19, there was an increase in bicycle injury frequency and proportional shift toward more injuries involving individuals of female sex. Otherwise, injury patterns were largely unchanged. These results demonstrate the necessity of safety interventions tailored to community needs.

The Deterioration Risk Index: Developing and Piloting a Machine Learning Algorithm to Reduce Pediatric Inpatient Deterioration.

OBJECTIVES: Develop and deploy a disease cohort-based machine learning algorithm for timely identification of hospitalized pediatric patients at risk for clinical deterioration that outperforms our existing situational awareness program. DESIGN: Retrospective cohort study. SETTING: Nationwide Children's Hospital, a freestanding, quaternary-care, academic children's hospital in Columbus, OH. PATIENTS: All patients admitted to inpatient units participating in the preexisting situational awareness program from October 20, 2015, to December 31, 2019, excluding patients over 18 years old at admission and those with a neonatal ICU stay during their hospitalization. INTERVENTIONS: We developed separate algorithms for cardiac, malignancy, and general cohorts via lasso-regularized logistic regression. Candidate model predictors included vital signs, supplemental oxygen, nursing assessments, early warning scores, diagnoses, lab results, and situational awareness criteria. Model performance was characterized in clinical terms and compared with our previous situational awareness program based on a novel retrospective validation approach. Simulations with frontline staff, prior to clinical implementation, informed user experience and refined interdisciplinary workflows. Model implementation was piloted on cardiology and hospital medicine units in early 2021. MEASUREMENTS AND MAIN RESULTS: The Deterioration Risk Index (DRI) was 2.4 times as sensitive as our existing situational awareness program (sensitivities of 53% and 22%, respectively; p < 0.001) and required 2.3 times fewer alarms per detected event (121 DRI alarms per detected event vs 276 for existing program). Notable improvements were a four-fold sensitivity gain for the cardiac diagnostic cohort (73% vs 18%; p < 0.001) and a three-fold gain (81% vs 27%; p < 0.001) for the malignancy diagnostic cohort. Postimplementation pilot results over 18 months revealed a 77% reduction in deterioration events (three events observed vs 13.1 expected, p = 0.001). CONCLUSIONS: The etiology of pediatric inpatient deterioration requires acknowledgement of the unique pathophysiology among cardiology and oncology patients. Selection and weighting of diverse candidate risk factors via machine learning can produce a more sensitive early warning system for clinical deterioration. Leveraging preexisting situational awareness platforms and accounting for operational impacts of model implementation are key aspects to successful bedside translation.

Geovisualization and Spatial Analysis of Infant Mortality and Preterm Birth in Ohio, 2008-2015: Opportunities to Enhance Spatial Thinking.

OBJECTIVES: Geovisualization and spatial analysis are valuable tools for exploring and evaluating the complex social, economic, and environmental interactions that lead to spatial inequalities in health. The objective of this study was to describe spatial patterns of infant mortality and preterm birth in Ohio by using interactive mapping and spatial analysis. METHODS: We conducted a retrospective cohort study using Ohio vital statistics records from 2008-2015. We geocoded live births and infant deaths by using residential address at birth. We used multivariable logistic regression to adjust spatial and space-time cluster analyses that examined the geographic clustering of infant mortality and preterm birth and changes in spatial distribution over time. RESULTS: The overall infant mortality rate in Ohio during the study period was 6.55 per 1000 births; of 1 097 507 births, 10.3% (n = 112 552) were preterm. We found significant geographic clustering of both infant mortality and preterm birth centered on large urban areas. However, when known demographic risk factors were taken into account, urban clusters disappeared and, for preterm birth, new rural clusters appeared. CONCLUSIONS: Although many public health agencies have the capacity to create maps of health outcomes, complex spatial analysis and geovisualization techniques are still challenging for public health practitioners to use and understand. We found that actively engaging policymakers in reviewing results of the cluster analysis improved understanding of the processes driving spatial patterns of birth outcomes in the state.

The Vitals Risk Index-Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System.

INTRODUCTION: Pediatric in-hospital cardiac arrests and emergent transfers to the pediatric intensive care unit (ICU) represent a serious patient safety concern with associated increased morbidity and mortality. Some institutions have turned to the electronic health record and predictive analytics in search of earlier and more accurate detection of patients at risk for decompensation. METHODS: Objective electronic health record data from 2011 to 2017 was utilized to develop an automated early warning system score aimed at identifying hospitalized children at risk of clinical deterioration. Five vital sign measurements and supplemental oxygen requirement data were used to build the Vitals Risk Index (VRI) model, using multivariate logistic regression. We compared the VRI to the hospital's existing early warning system, an adaptation of Monaghan's Pediatric Early Warning Score system (PEWS). The patient population included hospitalized children 18 years of age and younger while being cared for outside of the ICU. This dataset included 158 case hospitalizations (102 emergent transfers to the ICU and 56 "code blue" events) and 135,597 control hospitalizations. RESULTS: When identifying deteriorating patients 2 hours before an event, there was no significant difference between Pediatric Early Warning Score and VRI's areas under the receiver operating characteristic curve at false-positive rates ≤ 10% (pAUC10 of 0.065 and 0.064, respectively; P = 0.74), a threshold chosen to compare the 2 approaches under clinically tolerable false-positive rates. CONCLUSIONS: The VRI represents an objective, simple, and automated predictive analytics tool for identifying hospitalized pediatric patients at risk of deteriorating outside of the ICU setting.

Relationship between cyanobacterial bloom impacted drinking water sources and hepatocellular carcinoma incidence rates.

Freshwater cyanobacterial blooms have increased in geographic distribution and intensity in recent decades worldwide. Cyanotoxins produced by many of these blooms, such as microcystins, are observed to play a role in tumor promotion and have been associated with increased liver cancer rates at the population level. Exposure occurs primarily via contaminated water (ingestion, inhalation, dermal contact), either from treated drinking water or during recreation in impacted surface waters; additional sources of exposure include consumption of fresh produce grown in cyanotoxin-contaminated environments or through the consumption of seafood caught in bloom-impacted waters. The current ecological study investigates whether populations served by cyanobacterial bloom-impacted surface waters for their drinking water source have higher hepatocellular carcinoma (HCC) incidence rates than those served by non-impacted surface waters and groundwater. Census tract level cancer incidence in the state of Ohio, United States was modeled using a negative binomial generalized linear model, controlling for differences in demographic composition (e.g. age, race, and income) at the census tract level. Presence of cyanobacterial blooms in surface waters was estimated using satellite multi-spectral remote sensing and in situ public water system cyanotoxin monitoring data. Census tracts estimated to be served by bloom-impacted surface waters had 14.2% higher HCC incidence rates than those served by non-bloom-impacted surface waters (incidence rate ratio, IRR: 1.142; 95% CI: 1.037-1.257). Additionally, these bloom-impacted census tracts had a 17.4% higher HCC incidence rate as compared to those estimated to receive drinking water from a groundwater source (IRR: 1.174; 95% CI: 1.101-1.252). No statistical difference was found in HCC incidence rates when comparing areas presumed to be served by non-bloom-impacted surface waters and those presumed to be served by groundwater sources. An important consideration for environmental justice, areas estimated to be served by bloom-impacted surface waters had higher levels of poverty and included a higher percentage of racial and ethnic minority populations than areas served by groundwater. These findings support the need for additional in-depth research into the potential hepatic carcinogenicity and exposures of cyanotoxins in those areas where severe blooms are chronically observed.

Factors associated with psychiatric readmission of children and adolescents in the U.S.: A systematic review of the literature.

OBJECTIVE: A systematic review of research assessing factors associated with inpatient psychiatric readmission of children and adolescents. METHODS: In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we searched 8 databases (1994-2018) to identify relevant articles on factors associated with youth psychiatric readmission. Selected articles addressed one or more factors associated with psychiatric readmission for children and adolescents (≤21 years of age) admitted to a psychiatric hospital in the United States for a primary mental health diagnosis. Two authors independently reviewed article abstracts, titles, and text. RESULTS: Of 7903 retrieved articles, 30 studies met inclusion criteria. Analyzed variables were categorized according to child demographic and clinical characteristics; family, provider, and community characteristics; and treatment and aftercare characteristics. Available studies were markedly heterogeneous in methodology and outcomes. Factors associated with an increased risk of readmission included greater symptom severity, clinical diagnoses such as psychosis and affective disorders, suicidal behavior and self-injury, poor family functioning, and longer lengths of index hospital stay. CONCLUSIONS: Controlled trials of interventions to improve care and reduce recidivism for psychiatrically hospitalized youth are needed. Future research will benefit from a guiding theoretical framework, more representative samples, and standardized exposure/outcome measures.

Characterization of Cyanophages in Lake Erie: Interaction Mechanisms and Structural Damage of Toxic Cyanobacteria.

Cyanophages are abundant in aquatic environments and play a critical role in bloom dynamics, including regulation of cyanobacteria growth and photosynthesis. In this study, cyanophages from western Lake Erie water samples were screened for lytic activities against the host cell (Microcystis aeruginosa), which was also originated from Lake Erie and identified with real-time sequencing (Nanopore sequencing). M. aeruginosa was mixed with the cyanophages and their dynamic interactions were examined over two weeks using atomic force microscopy (AFM) as well as transmission electron microscopy (TEM), qPCR, phycocyanin and chlorophyll-a production, and optical absorbance measurements. The TEM images revealed a short-tailed virus (Podoviridae) in 300 nm size with unique capsid, knob-like proteins. The psbA gene and one knob-like protein gene, gp58, were identified by PCR. The AFM showed a reduction of mechanical stiffness in the host cell membranes over time after infection, before structural damage became visible. Significant inhibition of the host growth and photosynthesis was observed from the measurements of phycocyanin and chlorophyll-a concentrations. The results provide an insight into cyanobacteria-cyanophage interactions in bloom dynamics and a potential application of cyanophages for bloom control in specific situations.

Ten-year survey of cyanobacterial blooms in Ohio's waterbodies using satellite remote sensing.

Cyanobacterial blooms are on the rise globally and are capable of adversely impacting human, animal, and ecosystem health. Blooms dominated by cyanobacteria species capable of toxin-production are commonly observed in eutrophic freshwater. The presence of cyanobacterial blooms in selected Ohio lakes, such as Lake Erie and Grand Lake St. Marys, has been well studied, but much less is known about the geographic distribution of these blooms across all of Ohio's waterbodies. We examined the geographic distribution of cyanobacterial blooms in Ohio's waterbodies from 2002 to 2011, using a nested semi-empirical algorithm and remotely sensed data from the Medium Resolution Imaging Spectrometer (MERIS) onboard the European Space Agency's Envisat. We identified: 62 lakes, reservoirs, and ponds; 7 rivers; 6 marshes and wetlands; and 3 quarries with detectable cyanobacteria pigment (phycocyanin) concentrations. Of the 78 waterbodies identified in our study, roughly half (54%; n=42) have any reported in situ microcystins monitoring results from state monitoring programs. Further, 90% of the waterbodies identified reached phycocyanin pigment concentrations representative of levels potentially hazardous to public health. This gap in lakes potentially impacted by cyanobacterial blooms and those that are currently monitored presents an important area of concern for public health, as well as ecosystem health, where unknown human and animal exposures to cyanotoxins may occur in many of Ohio's waterbodies. Our approach may be replicated in other regions around the globe with potential cyanobacterial bloom presence, in order to assess the intensity, geographic distribution, and temporal pattern of blooms in lakes not currently monitored for the presence of cyanobacterial blooms.

Water Access, Sanitation, and Hygiene Conditions and Health Outcomes among Two Settlement Types in Rural Far North Cameroon.

The Far North region in Cameroon has been more heavily impacted by cholera than any other region over the past decade, but very little has been done to study the drivers of waterborne diseases in the region. We investigated the relationship between water, sanitation, and hygiene (WASH) parameters, microbial and antibiotic resistance (AR) contamination levels in drinking water, and health outcomes using health survey and molecular analysis during June and July of 2014 in two settlement types (agro-pastoralist villages and transhumant pastoralist camps). Quantitative polymerase chain reaction was used to determine fecal contamination sources, enteric pathogens, and antibiotic resistance genes. Ruminant-associated fecal contamination was widespread in both settlement types (81.2%), with human-associated contamination detected in 21.7% of the samples. Salmonella spp. (59.4%) and Shiga toxin-producing E. coli (stx1 44.9% and stx2 31.9%) were detected across all samples. Tetracycline resistance was found only in village samples. A significant difference in diarrheal incidence within the past 28 days among young children was found between camps (31.3%) and villages (0.0%). Our findings suggest that water contamination may play an important role in contributing to gastrointestinal illness, supporting the need for future research and public health intervention to reduce gastrointestinal illness in the area.