Evaluating Population Coverage in a Regional Distributed Data Network: Implications for Electronic Health Record-Based Public Health Surveillance

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

The target trial framework has emerged as a powerful tool for addressing causal questions in clinical practice and in public health. In the healthcare sector, where decision-making is increasingly data-driven, transactional databases, such as electronic health records (EHR) and insurance claims, present an untapped potential for answering complex causal questions. This narrative review explores the potential of the integration of the target trial framework with real-world data to enhance healthcare decision-making processes. We outline essential elements of the target trial framework, and identify pertinent challenges in data quality, privacy concerns, and methodological limitations, proposing solutions to overcome these obstacles and optimize the framework's application.

A process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records

Introduction

Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates.

Methods

We operationalized a two-step deduplication process, leveraging health information exchange (HIE)-assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0-17 years) with at least one 2017 encounter in one of two geographically-proximate DDN partners. We assessed the extent of cross-system duplication and its effect on prevalence estimates.

Results

We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute.

Conclusions

We implemented an HIE-dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.

Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations

Cardiovascular disease surveillance involves quantifying the evolving population-level burden of cardiovascular outcomes and risk factors as a data-driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more "national" surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care-seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information-gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

Trends in Outpatient Medical-Care Seeking for Acute Gastroenteritis During the COVID-19 Pandemic, 2020

The rate of enteric infections reported to public health surveillance decreased during 2020 amid the coronavirus disease 2019 (COVID-19) pandemic. Changes in medical care-seeking behaviors may have impacted the diagnosis of enteric infections contributing to these declines. We examined trends in outpatient medical care-seeking behavior for acute gastroenteritis (AGE) in Colorado during 2020 compared with the that of previous 3 years using electronic health record data from the Colorado Health Observation Regional Data Service (CHORDS). Outpatient medical encounters for AGE were identified using diagnoses codes from the International Classification of Diseases 10th Revision and aggregated by year, quarter, age group, and encounter type. The rate of encounters was calculated by dividing the number of AGE encounters by the corresponding total number of encounters. There were 9064 AGE encounters in 2020 compared with an annual average of 18,784 from 2017 to 2019 (p < 0.01), representing a 52% decrease. The rate of AGE encounters declined after the first quarter of 2020 and remained significantly lower for the rest of the year. Moreover, previously observed trends, including seasonal patterns and the preponderance of pediatric encounters, were no longer evident. Telemedicine modalities accounted for 23% of all AGE encounters in 2020. AGE outpatient encounters in Colorado in 2020 were substantially lower than during the previous 3 years. Decreases remained stable over the second, third, and fourth quarters of 2020 (April-December) and were especially pronounced for children <18 years of age. Changes in medical care-seeking behavior likely contributed to declines in the number of enteric disease cases and outbreaks reported to public health. It is unclear to what extent people were ill with AGE and did not seek medical care because of concerns about the infection risk during a health care visit or to what extent there were reductions in certain exposures and opportunities for disease transmission resulting in less illness.