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Objective: Electronic health records (EHRs) hold promise as a public health surveillance tool, but questions remain about how EHR patients compare with populations in health and demographic surveys. We compared population characteristics from a regional distributed data network (DDN), which securely and confidentially aggregates EHR data from multiple health care organizations in the same geographic region, with population characteristics from health and demographic surveys.
Methods: Ten health care organizations participating in a Colorado DDN contributed data for coverage estimation. We aggregated demographic and geographic data from 2017 for patients aged ≥18 residing in 7 counties. We used a cross-sectional design to compare DDN population size, by county, with the following survey-estimated populations: the county population, estimated by the American Community Survey (ACS); residents seeking any health care, estimated by the Colorado Health Access Survey; and residents seeking routine (eg, primary) health care, estimated by the Behavioral Risk Factor Surveillance System. We also compared data on the DDN and survey populations by sex, age group, race/ethnicity, and poverty level to assess surveillance system representativeness.
Results: The DDN population included 609 840 people in 7 counties, corresponding to 25% coverage of the general adult population. Population coverage ranged from 15% to 35% across counties. Demographic distributions generated by DDN and surveys were similar for many groups. Overall, the DDN and surveys assessing care-seeking populations had a higher proportion of women and older adults than the ACS population. The DDN included higher proportions of Hispanic people and people living in high-poverty neighborhoods compared with the surveys.
Conclusion: The DDN population is not a random sample of the regional adult population; it is influenced by health care use patterns and organizations participating in the DDN. Strengths and limitations of DDNs complement those of survey-based approaches. The regional DDN is a promising public health surveillance tool.
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http://dx.doi.org/10.1177/0033354920941158 | DOI Listing |
Sud Med Ekspert
January 2025
Bureau of Forensic Medical Examination of the Department of Health Care of the City of Moscow, Moscow, Russia.
The article considers the main phases of traffic injury (TI) described by A.A. Solokhin in 1968 and their modern application in forensic medical and automotive examination.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
School of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.
Background: Owing to the unique characteristics of digital health interventions (DHIs), a tailored approach to economic evaluation is needed-one that is distinct from that used for pharmacotherapy. However, the absence of clear guidelines in this area is a substantial gap in the evaluation framework.
Objective: This study aims to systematically review and compare the economic evaluation literature on DHIs and pharmacotherapy for the treatment of depression.
JMIR Hum Factors
September 2025
Department of Community Health Systems, University of California, San Francisco, School of Nursing, San Francisco, CA, United States.
Background: The COVID-19 pandemic forced the world to quarantine to slow the rate of transmission, causing communities to transition into virtual spaces. Asian American and Pacific Islander communities faced the additional challenge of discrimination that stemmed from racist and xenophobic rhetoric in the media. Limited data exist on technology use among Asian American and Pacific Islander adults during the height of the COVID-19 shelter-in-place period and its effect on their physical and mental health.
View Article and Find Full Text PDFJMIR Hum Factors
September 2025
KK Women's and Children's Hospital, Singapore, Singapore.
Background: Breast cancer treatment, particularly during the perioperative period, is often accompanied by significant psychological distress, including anxiety and uncertainty. Mobile health (mHealth) interventions have emerged as promising tools to provide timely psychosocial support through convenient, flexible, and personalized platforms. While research has explored the use of mHealth in breast cancer prevention, care management, and survivorship, few studies have examined patients' experiences with mobile interventions during the perioperative phase of breast cancer treatment.
View Article and Find Full Text PDF