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A Bayesian spatio-temporal analysis of neighborhood pediatric asthma emergency department visit disparities.

ABSTRACT: Asthma disparities have complex, neighborhood-level drivers that are not well understood. Consequently, identifying particular contextual factors that contribute to disparities is a public health goal. We study pediatric asthma emergency department (ED) visit disparities and neighborhood factors associated with them in South Carolina (SC) census tracts from 1999 to 2015. Leveraging a Bayesian framework, we identify risk clusters, spatially-varying relationships, and risk percentile-specific associations. Clusters of high risk occur in both rural and urban census tracts with high probability, with neighborhood-specific associations suggesting unique risk factors for each locale. Bayesian methods can help clarify the neighborhood drivers of health disparities.

SUBMITTER: Bozigar M

PROVIDER: S-EPMC8591955 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A Bayesian spatio-temporal analysis of neighborhood pediatric asthma emergency department visit disparities.

Bozigar Matthew M Lawson Andrew B AB Pearce John L JL King Kathryn K Svendsen Erik R ER

Health & place 20201001

Asthma disparities have complex, neighborhood-level drivers that are not well understood. Consequently, identifying particular contextual factors that contribute to disparities is a public health goal. We study pediatric asthma emergency department (ED) visit disparities and neighborhood factors associated with them in South Carolina (SC) census tracts from 1999 to 2015. Leveraging a Bayesian framework, we identify risk clusters, spatially-varying relationships, and risk percentile-specific asso ...[more]

PMID: 33011491

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Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Project description:ImportanceMany children and adolescents in the United States are exposed to neighborhood gun violence. Associations between violence exposure and children's short-term mental health are not well understood.ObjectiveTo examine the association between neighborhood gun violence and subsequent mental health-related pediatric emergency department (ED) utilization.Design, setting, and participantsThis location-based cross-sectional study included 128 683 ED encounters for children aged 0 to 19 years living in 12 zip codes in Philadelphia, Pennsylvania, who presented to an urban academic pediatric ED from January 1, 2014, to December 31, 2018. Children were included if they (1) had 1 or more ED visits in the 60 days before or after a neighborhood shooting and (2) lived within a quarter-mile radius of the location where this shooting occurred. Analysis began August 2020 and ended May 2021.ExposureNeighborhood violence exposure, as measured by whether a patient resided near 1 or more episodes of police-reported gun violence.Main outcomes and measuresED encounters for a mental health-related chief complaint or primary diagnosis.ResultsA total of 2629 people were shot in the study area between 2014 and 2018, and 54 341 children living nearby had 1 or more ED visits within 60 days of a shooting. The majority of these children were Black (45 946 [84.5%]) and were insured by Medicaid (42 480 [78.1%]). After adjusting for age, sex, race and ethnicity, median household income by zip code, and insurance, children residing within one-eighth of a mile (2-3 blocks) of a shooting had greater odds of mental health-related ED presentations in the subsequent 14 days (adjusted odds ratio, 1.86 [95% CI, 1.20-2.88]), 30 days (adjusted odds ratio, 1.49 [95% CI, 1.11-2.03]), and 60 days (adjusted odds ratio, 1.35 [95% CI, 1.06-1.72]).Conclusions and relevanceExposure to neighborhood gun violence is associated with an increase in children's acute mental health symptoms. City health departments and pediatric health care systems should work together to provide community-based support for children and families exposed to violence and trauma-informed care for the subset of these children who subsequently present to the ED. Policies aimed at reducing children's exposure to neighborhood gun violence and mitigating the mental symptoms associated with gun violence exposure must be a public health priority.

Dataset Information

A Bayesian spatio-temporal analysis of neighborhood pediatric asthma emergency department visit disparities.

Publications

A Bayesian spatio-temporal analysis of neighborhood pediatric asthma emergency department visit disparities.

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