A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

Geospatial data in pediatric asthma in the United States: a scoping review protocol

OBJECTIVE

The objective of this scoping review is to identify and describe the literature on the use of geospatial data in pediatric asthma research.

INTRODUCTION

Asthma is one of the most common pediatric chronic diseases in the United States, disproportionately affecting low-income patients. Asthma exacerbations may be triggered by local environmental factors, such as air pollution or exposure to indoor allergens. Geographic information systems are increasingly recognized as tools that use geospatial data to enhance understanding of the link between environmental exposure, social determinants of health, and clinical outcomes. Geospatial data in pediatric asthma may help inform risk factors for asthma severity, and guide targeted clinical and social interventions.

INCLUSION CRITERIA

This review will consider studies that utilize geospatial data in the evaluation of pediatric patients with asthma, ages 2 to 18 years, in the United States. Mixed samples of adults and children will also be considered. Geospatial data will include any external non-clinical geographic-based data source that uses a patient's environment or context.

METHODS

The following databases will be searched: PubMed, Embase, Cochrane CENTRAL, CINAHL, ERIC, Web of Science, and IEEE. Gray literature will be searched in DBLP, the US Environmental Protection Agency, Google Scholar, Google search, and a hand search of recent abstracts from relevant conferences. Articles published in English, Spanish, and French from 2010 to the present will be included. Study screening and selection will be performed independently by 2 reviewers. Data extraction will be performed by a trained research team member following pilot testing.

Effectiveness of a targeted telephone-based case management service on activity in an Emergency Department in the UK: a pragmatic difference-in-differences evaluation

BACKGROUND

This study evaluates the effectiveness of a targeted telephone-based case management service that aimed to reduce ED attendance amongst frequent attenders, known to disproportionately contribute to demand. Evidence on the effectiveness of these services varies.

METHODS

A 24-month controlled before-and-after study, following 808 patients (128 cases and 680 controls (41 were non-compliant)) who were offered the service in the first four months of operation within a UK ED department. Patients stratified as high-risk of reattending ED within 6 months by a predictive model were manually screened. Those positively reviewed were offered a non-clinical, nurse-led, telephone-based health coaching, consisting of care planning, coordination and goal setting for up to 9 months. Service effectiveness was estimated using a difference-in-differences (DiD) analysis. Incident rate of ED and Minor Injury Unit (MIU) attendances and average length of stay in intervention recipients and controls over 12 months after receiving their service offer following ED attendance were compared, adjusting for the prior 12-month period, sex and age, to give an incidence rate ratio (IRR).

RESULTS

Intervention recipients were more likely to be female (63.3% versus 55.4%), younger (mean of 69 years versus 76 years), and have higher levels of ED activity (except for MIU) than controls. Mean rates fell between periods for all outcomes (except for MIU attendance). The Intention-to-Treat analysis indicated non-statistically significant effect of the intervention in reducing all outcomes, except for MIU attendances, with IRRs: ED attendances, 0.856 (95% CI: 0.631, 1.160); ED admissions, 0.871 (95% CI: 0.628, 1.208); length of stay for emergency and elective admissions: 0.844 (95% CI: 0.619, 1.151) and 0.781 (95% CI: 0.420, 1.454). MIU attendance increased with an IRR: 2.638 (95% CI: 1.041, 6.680).

CONCLUSIONS

Telephone-based health coaching appears to be effective in reducing ED attendances and admissions, with shorter lengths of stay, in intervention recipients over controls. Future studies need to capture outcomes beyond acute activity, and better understand how services like this provide added value.

Using Bayesian time-stratified case-crossover models to examine associations between air pollution and "asthma seasons" in a low air pollution environment

Many areas of the United States have air pollution levels typically below Environmental Protection Agency (EPA) regulatory limits. Most health effects studies of air pollution use meteorological (e.g., warm/cool) or astronomical (e.g., solstice/equinox) definitions of seasons despite evidence suggesting temporally-misaligned intra-annual periods of relative asthma burden (i.e., “asthma seasons”). We introduce asthma seasons to elucidate whether air pollutants are associated with seasonal differences in asthma emergency department (ED) visits in a low air pollution environment. Within a Bayesian time-stratified case-crossover framework, we quantify seasonal associations between highly resolved estimates of six criteria air pollutants, two weather variables, and asthma ED visits among 66,092 children ages 5–19 living in South Carolina (SC) census tracts from 2005 to 2014. Results show that coarse particulates (particulate matter <10 μm and >2.5 μm: PM_10-2.5) and nitrogen oxides (NO_x) may contribute to asthma ED visits across years, but are particularly implicated in the highest-burden fall asthma season. Fine particulate matter (<2.5 μm: PM_2.5) is only associated in the lowest-burden summer asthma season. Relatively cool and dry conditions in the summer asthma season and increased temperatures in the spring and fall asthma seasons are associated with increased ED visit odds. Few significant associations in the medium-burden winter and medium-high-burden spring asthma seasons suggest other ED visit drivers (e.g., viral infections) for each, respectively. Across rural and urban areas characterized by generally low air pollution levels, there are acute health effects associated with particulate matter, but only in the summer and fall asthma seasons and differing by PM size.

Correction to: A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

Unfortunately, the original version of the article [1] contained an error. A typo in the main equation (Eq. 1) has been introduced during the production process. The operator " = " in Eq. 1 "log(θ_ik) =  α + u_i…" was missing.