Search

Introduction: Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness., Methods: We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues., Results: We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values., Conclusion: Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Objectives: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7 ® FHIR ® ) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline., Materials and Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database., Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed., Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data., Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data., Competing Interests: B.Z. and J.A. are affiliated with an organization that has funding from the Massachusetts Department of Public Health for support and development of Electronic Medical Record Support for Public Health (ESP) and MDPHnet, which is the underlying technology of MENDS. All other authors declare no competing interests. No copyrighted materials were used in this article., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Introduction: Modernizing chronic disease surveillance with electronic health record (EHR) data may provide better data to improve hypertension prevention and control, but no consensus exists for an EHR-based surveillance definition for hypertension. The Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot surveillance system was used to develop and test an electronic phenotype for hypertension., Methods: We used MENDS data from 1,671,279 patients in Louisiana to examine the effect of different analytic decisions on estimates of hypertension prevalence. Decisions included 1) whether to restrict surveillance to patients with recent blood pressure measurements, 2) varying the number and recency of encounters to define the population at risk of hypertension, 3) how to define hypertension (diagnosis codes, antihypertensive medication, blood pressure measurements, or combinations of these), and 4) how to handle multiple blood pressure measurements on the same day. Results were compared with independent estimates of hypertension prevalence in Louisiana from the Behavioral Risk Factor Surveillance System (BRFSS)., Results: Applying varying criteria resulted in hypertension prevalence estimates ranging from 19.7% to 59.3%. A hypertension surveillance strategy that includes a population with at least 1 clinical encounter with measured blood pressure in the previous 2 years and identifies hypertension using all available data (≥1 diagnosis code, ≥1 antihypertensive medication, and ≥2 elevated blood pressure values ≥140/90 mm Hg on separate days) generated estimates in line with population-based survey data. This definition estimated the crude 2019 hypertension prevalence in the state of Louisiana as 43.4% (age-adjusted, 41.0%), comparable with the crude BRFSS estimate of 39.7% (age adjusted, 37.1%)., Conclusion: Applying different criteria to define hypertension using EHR data has a large effect on hypertension prevalence estimates. The proposed electronic phenotype generates hypertension prevalence estimates that align with independent estimates from BRFSS.

Purpose: Increased physical activity (PA) levels in cancer survivors are associated with decreased risk of recurrence and mortality as well as additional positive health outcomes. PA interventions have shown to be efficacious, though many lack translation to and sustainability in community settings. We used dimensions of the RE-AIM framework to evaluate LIVESTRONG® at the YMCA, a nation-wide community-based PA program for cancer survivors delivered at Ys., Methods: This was a longitudinal study design using national LIVESTRONG at the YMCA data compiled between 2010 and 2018. Data is from all YMCAs who deliver LIVESTRONG at the YMCA, submitted by Program Directors to the YMCA-USA. We assessed reach (number of participants), adoption (associations offering the program), implementation (conducting 3 fidelity checks), and organizational level maintenance (associations recently offering program). We also examined relationships between organizational characteristics (years of program existence and association area household income) and program implementation factors with member conversion rates., Results: As of 2018, LIVESTRONG at the YMCA has reached 62,044 survivors and 245 of the 840 (29.2%) of Y associations have adopted the program. Among the adopters, 91% were aware of fidelity checks; implementation of observational (62.3%), goal setting (49.9%), and functional (64.6%) checklists varied. Most (95.1%) adopters reported offering ≥ 1 LIVESTRONG session per year (organizational-level maintenance) and a facility-level mean membership conversion percentage of 46.9 ± 31.2%. Fewer years implementing the program and higher association area household income were significantly associated with a greater membership conversion rate vs their comparison. In a multiple regression model controlling for organizational characteristics, conducting the fidelity checks independently (observational, β = 8.41; goal-setting, β = 9.70; and functional, β = 9.61) and collectively ( β = 10.82; 95% CI 5.90-16.80) was positively associated with higher membership conversion rates., Conclusions: LIVESTRONG at the YMCA, in its early years, has shown promise for high reach, while adoption at more associations could be facilitated. Implementing fidelity checks along with organizational characteristics were associated with membership conversion rate. Identification of association-level strategies to increase reach, adoption, implementation, and maintenance may increase the impact of this community-based PA program., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Background: United States pediatric guidelines recommend that childhood obesity counseling be conducted in the primary care setting. Primary care-based interventions can be effective in improving health behaviors, but also costly. The purpose of this study was to evaluate the cost of a primary care-based obesity prevention intervention targeting children between the ages of two and six years who are at elevated risk for obesity, measured against usual care., Methods: High Five for Kids was a cluster-randomized controlled clinical trial that aimed to modify children's nutrition and TV viewing habits through a motivational interviewing intervention. We assessed visit-related costs from a societal perspective, including provider-incurred direct medical costs, provider-incurred equipment costs, parent time costs and parent out-of-pocket costs, in 2011 dollars for the intervention (n = 253) and usual care (n =192) groups. We conducted a net cost analysis using both societal and health plan costing perspectives and conducted one-way sensitivity and uncertainty analyses on results., Results: The total costs for the intervention group and usual care groups in the first year of the intervention were $65,643 (95% CI [$64,522, $66,842]) and $12,192 (95% CI [$11,393, $13,174]). The mean costs for the intervention and usual care groups were $259 (95% CI [$255, $264]) and $63 (95% CI [$59, $69]) per child, respectively, for a incremental difference of $196 (95% CI [$191, $202]) per child. Children in the intervention group attended a mean of 2.4 of a possible 4 in-person visits and received 0.45 of a possible 2 counseling phone calls. Provider-incurred costs were the primary driver of cost estimates in sensitivity analyses., Conclusions: High Five for Kids was a resource-intensive intervention. Further studies are needed to assess the cost-effectiveness of the intervention relative to other pediatric obesity interventions.

The purpose of this study was to examine the correlates of participation in a childhood obesity prevention trial. We sampled parents of children recruited to participate in a randomized controlled trial. Eligible children were 2.0-6.9 years with BMI ≥ 95th percentile or 85th to <95th percentile if at least one parent was overweight. We attempted contact with parents of children who were potentially eligible. We recruited 475 parents via telephone following an introductory letter. We also interviewed 329 parents who refused participation. Parents who refused participation (n = 329) did not differ from those who participated (n = 475) by number of children at home (OR 0.94 per child; 95% CI: 0.77-1.15) or by child age (OR 1.07 per year; 95% CI: 0.95-1.20) or sex (OR 1.06 for females vs. males; 95% CI: 0.80-1.41). After multivariate adjustment, parents who were college graduates vs. 95th%ile. One reason appears to be that they less frequently consider their children to have a weight problem.