Preventing Chronic Disease最新文献

Background: Data modernization efforts to strengthen surveillance capacity could help assess trends in use of preventive services and diagnoses of new chronic disease during the COVID-19 pandemic, which broadly disrupted health care access.

Methods: This cross-sectional study examined electronic health record data from US adults aged 21 to 79 years in a large national research network (PCORnet), to describe use of 8 preventive health services (N = 30,783,825 patients) and new diagnoses of 9 chronic diseases (N = 31,588,222 patients) during 2018 through 2022. Joinpoint regression assessed significant trends, and health debt was calculated comparing 2020 through 2022 volume to prepandemic (2018 and 2019) levels.

Results: From 2018 to 2022, use of some preventive services increased (hemoglobin A_1c and lung computed tomography, both P < .05), others remained consistent (lipid testing, wellness visits, mammograms, Papanicolaou tests or human papillomavirus tests, stool-based screening), and colonoscopies or sigmoidoscopies declined (P < .01). Annual new chronic disease diagnoses were mostly stable (6% hypertension; 4% to 5% cholesterol; 4% diabetes; 1% colonic adenoma; 0.1% colorectal cancer; among women, 0.5% breast cancer), although some declined (lung cancer, cervical intraepithelial neoplasia or carcinoma in situ, cervical cancer, all P < .05). The pandemic resulted in health debt, because use of most preventive services and new diagnoses of chronic disease were less than expected during 2020; these partially rebounded in subsequent years. Colorectal screening and colonic adenoma detection by age group aligned with screening recommendation age changes during this period.

Conclusion: Among over 30 million patients receiving care during 2018 through 2022, use of preventive services and new diagnoses of chronic disease declined in 2020 and then rebounded, with some remaining health debt. These data highlight opportunities to augment traditional surveillance with EHR-based data.

Introduction: After SARS-CoV-2 infection, some people will experience long-term sequelae known as post-COVID-19 condition (PCC). Although PCC is recognized as a public health problem, estimates of the prevalence of PCC are sparse. We described a framework for estimating the incidence and prevalence of PCC by population subgroups and geography over time in Washington State.

Methods: We collected data on reported COVID-19 cases and hospitalizations and estimated SARS-CoV-2 infections in Washington State from March 2020 through October 2023. The reported case data were incorporated with parameter estimates from published articles and prevalence estimates from the Household Pulse Survey into a mathematical compartmental model of PCC progression. The model used differential equations to describe how the population of people with PCC moved through the model's various stages. This framework allowed us to integrate data on age group, sex, race and ethnicity, vaccination status, and county to estimate incidence and prevalence of PCC for each subgroup.

Results: Our model indicated that 6.4% (95% CI, 5.9%-6.8%) of all adults in Washington State were experiencing PCC as of October 2023. In addition to temporal differences in PCC prevalence and incidence, we found substantial differences across age groups, race and ethnicity, and sex. Geographic heterogeneity was pronounced, with the highest rates of PCC in central and eastern Washington.

Conclusion: Estimation of PCC prevalence is essential for addressing PCC as a public health problem. Responding to PCC will require continued surveillance, research, and dedicated financial and public health action. This analysis, accounting for heterogeneities, highlights disparities in the prevalence, incidence, and distribution of PCC in Washington State and can better guide awareness and response efforts.

Easy access and display of state-level estimates of the prevalence of chronic diseases and their risk factors can guide evidence-based decision-making, policy development, and tailored efforts to improve population health outcomes; however, these estimates are often presented across multiple websites and reports. The Chronic Disease Indicators (CDI) web tool (www.cdc.gov/cdi) disseminates state-level data compiled from various data sources, including surveys, vital records, and administrative data, and applies standardized definitions to estimate and track a wide range of key indicators of chronic diseases and their risk factors. In 2022-2024, the indicators were refreshed to include 113 measures across 21 topic areas, and the web tool was modernized to enhance its key features and functionalities, including standardized indicator definitions; interactive charts, graphs, and maps that present data in a visually appealing format; an easy-to-use web-based interface for users to query and extract the data they need; and state comparison reports to identify geographic variations in disease and risk factor prevalence. National and state-level estimates are provided for the overall population and, where applicable, by sex, race and ethnicity, and age. We review the history of CDIs, describe the 2022-2024 refresh process, and explore the interactive features of the CDI web tool with the goal of demonstrating how practitioners, policymakers, and other users can easily examine and track a wide range of key indicators of chronic diseases and their risk factors to support state-level public health action.

Built environment approaches that improve active transportation infrastructure and environmental design can increase physical activity. Funded by the Centers for Disease Control and Prevention, the Texas Department of State Health Services rejuvenated the Texas Plan4Health program from 2018 to 2023 to expand such approaches in Texas by providing technical assistance to teams of local public health professionals and planners to identify and implement projects connecting people to everyday destinations via active transport in their communities. However, the COVID-19 pandemic prompted Texas Plan4Health to modify the delivery of technical assistance to accommodate restrictions on travel and in-person gatherings. We used qualitative methods to conduct a postintervention process evaluation to describe the modified technical assistance process, understand the experiences of the 4 participating communities, and identify short-term outcomes and lessons learned. Texas Plan4Health helped communities overcome common barriers to built environment change, facilitated collaboration across community public health and planning professionals, and educated professionals about active transportation infrastructure and the relationship between their disciplines, thereby increasing community capacity to implement built environment improvements. This outcome, however, was mediated by the pre-existing resources and previous experiences with active transportation planning among the participating communities. Public health practitioners seeking to improve active transportation infrastructure and environmental design for physical activity should consider community-engaged approaches that advance partnership-building and collaborative experiential education among public health, planning, and other local government representatives, directing particular attention and additional training toward communities with fewer resources.

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.

[This corrects the article DOI: 10.5888/pcd21.230277.].

We examined participation rates, engagement, and weight-loss outcomes of comparison group participants in a diabetes prevention trial who enrolled in a digitally delivered diabetes prevention program (ie, an active intervention) after the original trial ended. We evaluated these outcomes by using the Wilcoxon signed-rank test and 1-sample z test. We found a high participation rate (73%) among comparison group participants and comparable weight-loss outcomes at 12 months (6.8 lb) after initiating participation in the active intervention relative to intervention group participants during the original trial. Findings support providing evidence-based interventions for comparison or control group participants post-trial. Findings also support examining the cost-effectiveness of post-trial interventions, regardless of the limitations of acquiring post-trial data on weight in an uncontrolled setting.

Michigan's CHRONICLE, the Chronic Disease Registry Linking Electronic Health Record Data, is a near-real-time disease monitoring system designed to harness electronic health record (EHR) data and existing health information exchange (HIE) infrastructure for transformative public health surveillance. Strong evidence indicates that using EHR data in chronic disease monitoring will provide rapid insight over time on health care use, outcomes, and public health interventions. We examined the potential of EHR data for chronic disease surveillance through close collaboration with our statewide HIE network and 2 participating health systems. We describe the development of CHRONICLE, the promising findings from its implementation, the identified challenges, and how those challenges will inform the next steps in testing, refining, and expanding the system. By detailing our approach to developing CHRONICLE and the considerations and early steps required to build an innovative, EHR-based chronic disease registry, we aim to inform public health leaders and professionals on the value of EHR data for chronic disease surveillance. With systematic testing, evaluation, and enhancement, our goal for CHRONICLE, as a fully realized and comprehensive surveillance system, is to model how collaborative health information exchange can support evidence-based strategies, resource allocation, and precision in disease monitoring.