Mapping emergency department asthma visits to identify poor-quality housing in New Haven, CT, USA: a retrospective cohort study

Development of a multimodal geomarker pipeline to assess the impact of social, economic, and environmental factors on pediatric health outcomes

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.

Housing-Sensitive Health Conditions Can Predict Poor-Quality Housing

Improving housing quality may improve residents' health, but identifying buildings in poor repair is challenging. We developed a method to improve health-related building inspection targeting. Linking New York City Medicaid claims data to Landlord Watchlist data, we used machine learning to identify housing-sensitive health conditions correlated with a building's presence on the Watchlist. We identified twenty-three specific housing-sensitive health conditions in five broad categories consistent with the existing literature on housing and health. We used these results to generate a housing health index from building-level claims data that can be used to rank buildings by the likelihood that their poor quality is affecting residents' health. We found that buildings in the highest decile of the housing health index (controlling for building size, community district, and subsidization status) scored worse across a variety of housing quality indicators, validating our approach. We discuss how the housing health index could be used by local governments to target building inspections with a focus on improving health.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Emergency department use and geospatial variation in social determinants of health: a pilot study from South Carolina

BACKGROUND

Health systems are increasingly addressing patients' social determinants of health (SDoH)-related needs and investigating their effects on health resource use. SDoH needs vary geographically; however, little is known about how this geographic variation in SDoH needs impacts the relationship between SDoH needs and health resource use.

METHODS

This study uses data from a SDoH survey administered to a pilot patient population in a single health system and the electronic medical records of the surveyed patients to determine if the impact of SDoH needs on emergency department use varies geospatially at the US Census block group level. A Bayesian zero-inflated negative binomial model was used to determine if emergency department visits after SDoH screening varied across block groups. Additionally, the relationships between the number of emergency department visits and the response to each SDoH screening question was assessed using Bayesian negative binomial hurdle models with spatially varying coefficients following a conditional autoregressive (CAR) model at the census block group level.

RESULTS

Statistically important differences in emergency department visits after screening were found between block groups. Statistically important spatial variation was found in the association between patient responses to the questions concerning unhealthy home environments (e.g. mold, bugs/rodents, not enough air conditioning/heat) or domestic violence/abuse and the mean number of emergency department visits after the screen.

CONCLUSIONS

Notable spatial variation was found in the relationships between screening positive for unhealthy home environments or domestic violence/abuse and emergency department use. Despite the limitation of a relatively small sample size, sensitivity analyses suggest spatially varying relationships between other SDoH-related needs and emergency department use.

Implementation of standardized asthma management programs in outpatient settings

PURPOSE OF REVIEW

This article reviews new approaches, facilitators, barriers, and opportunities to increasing adoption of standardized asthma management programs in the outpatient care setting.

RECENT FINDINGS

Primary care clinicians providing asthma care in the outpatient setting are challenged by the complexity of guidelines and want standardization of tools that are easy to use and that can be integrated within their practice's workflow. Programs that integrate clinical decision support tools within a practice's electronic health record and provide support from specialists may enhance uptake of asthma management programs in the outpatient setting and reduce asthma morbidity. Lack of an implementation science framework, consideration for organizational context, and clinician buy-in are recently recognized barriers to adoption of asthma programs and improved asthma outcomes. In addition, many of these interventions are labor intensive, costly, and may not be capable of wide dissemination because of the EHR interoperability problem.

CONCLUSION

Programs that simplify the guidelines, integrate clinical decision support within the EHR, and ground their approach with an implementation science framework may improve the quality of asthma care provided in the outpatient setting.