El papel de los centros para el control y la prevención de enfermedades en la vigilancia de enfermedades crónicas en los Estados Unidos
DOI:
https://doi.org/10.5281/zenodo.18755694Palabras clave:
Centros para el Control y la Prevención de Enfermedades, Estados Unidos, Monitoreo Epidemiológico, Enfermedades CrónicasResumen
Las enfermedades crónicas representan un importante desafío para la salud pública en Estados Unidos, representando una gran parte de la morbilidad, la mortalidad y los costos de la atención médica. Aproximadamente el 60% de los adultos estadounidenses viven con al menos una enfermedad crónica, y el 40% padece múltiples enfermedades simultáneamente. Dada esta prevalencia, se requiere una vigilancia sistemática para monitorear tendencias, identificar factores de riesgo y orientar las estrategias de prevención y manejo a nivel nacional y local. Los Centros para el Control y la Prevención de Enfermedades desempeñan un papel fundamental en este proceso a través de una extensa red de programas de vigilancia que rastrean la prevalencia, la incidencia y los factores asociados para enfermedades como las cardiopatías, el cáncer, los accidentes cerebrovasculares y la diabetes. Los CDC se basan en múltiples fuentes de datos, incluyendo encuestas, registros vitales y datos de salud electrónicos, que se integran en herramientas como la herramienta web Indicadores de Enfermedades Crónicas. Esta plataforma estandariza y centraliza indicadores clave para los profesionales de la salud pública. La modernización continua de los sistemas de datos, con énfasis en la integración de registros de salud electrónicos y el uso de tecnologías avanzadas como la inteligencia artificial y el análisis de datos a gran escala, mejora la precisión, el alcance y la puntualidad de la información recopilada. Esto apoya intervenciones de salud pública más específicas y oportunas, incluyendo enfoques emergentes de salud pública de precisión que incorporan factores genéticos, socioambientales y conductuales en estrategias de prevención personalizadas. Persisten desafíos persistentes, en particular en lo que respecta a la calidad de los datos, la interoperabilidad de los sistemas y la necesidad de capacitación continua del personal para garantizar el uso eficaz de estas herramientas.
Referencias
Abad, Z. S. H., Kline, A., Sultana, M., Noaeen, M., Nurmambetova, E., Lucini, F. R., Al-Jefri, M., & Lee, J. (2021). Digital public health surveillance: a systematic scoping review. Npj Digital Medicine, 4(1), 41. https://doi.org/10.1038/s41746-021-00407-6
Adekugbe, A. P., & Ibeh, C. V. (2024). HARNESSING DATA INSIGHTS FOR CRISIS MANAGEMENT IN U.S. PUBLIC HEALTH: LESSONS LEARNED AND FUTURE DIRECTIONS. International Medical Science Research Journal, 4(4), 391. https://doi.org/10.51594/imsrj.v4i4.998
Afzal, H. B., Jahangir, T., Mei, Y., Madden, A., Sarker, A., & Kim, S. (2024). Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1309490
Anjaria, P., Asediya, V., Bhavsar, P. P., Pathak, A., Desai, D., & Patil, V. (2023). Artificial Intelligence in Public Health: Revolutionizing Epidemiological Surveillance for Pandemic Preparedness and Equitable Vaccine Access. Vaccines, 11(7), 1154. https://doi.org/10.3390/vaccines11071154
Baker, J. L., & Bjerregaard, L. G. (2023). Advancing precision public health for obesity in children [Review of Advancing precision public health for obesity in children]. Reviews in Endocrine and Metabolic Disorders, 24(5), 1003. Springer Science+Business Media. https://doi.org/10.1007/s11154-023-09802-8
Barth, O., Anderson, B., Jones, K., Nickles, A., Dawkins, K., Burnett, A., & Quartermus, K. (2024). An Innovative Approach to Using Electronic Health Records Through Health Information Exchange to Build a Chronic Disease Registry in Michigan. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.230413
Bavli, I., & Galea, S. (2024). Key considerations in the adoption of Artificial Intelligence in public health. PLOS Digital Health, 3(7). https://doi.org/10.1371/journal.pdig.0000540
Bosward, R., Braunack‐Mayer, A., Frost, E. K., & Carter, S. M. (2025). The emergence and future of precision public health: a scoping review [Review of The emergence and future of precision public health: a scoping review]. Health Policy and Technology, 14(5), 101056. Elsevier BV. https://doi.org/10.1016/j.hlpt.2025.101056
Budhathoki, N., Bhandari, R., Bashyal, S., & Lee, C. (2023). Predicting asthma using imbalanced data modeling techniques: Evidence from 2019 Michigan BRFSS data. PLoS ONE, 18(12). https://doi.org/10.1371/journal.pone.0295427
Canfell, O. J., Davidson, K., Woods, L., Sullivan, C., Cocoros, N. M., Klompas, M., Zambarano, B., Eakin, E., Littlewood, R., & Burton‐Jones, A. (2022). Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases. Frontiers in Public Health, 10, 854525. https://doi.org/10.3389/fpubh.2022.854525
Canfell, O. J., Kodiyattu, Z., Eakin, E., Burton‐Jones, A., Wong, I., Macaulay, C., & Sullivan, C. (2022). Real-world data for precision public health of noncommunicable diseases: a scoping review [Review of Real-world data for precision public health of noncommunicable diseases: a scoping review]. BMC Public Health, 22(1). BioMed Central. https://doi.org/10.1186/s12889-022-14452-7
Carney, T. J., Wiltz, J. L., Davis, K., Briss, P. A., & Hacker, K. (2023). Advancing Chronic Disease Practice Through the CDC Data Modernization Initiative. Preventing Chronic Disease, 20. https://doi.org/10.5888/pcd20.230120
Chen, K. C., Abtahi, F., Carrero, J. J., Fernández-Llatas, C., Xu, H., & Seoane, F. (2024). Validation of an interactive process mining methodology for clinical epidemiology through a cohort study on chronic kidney disease progression. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-79704-5
Dhanda, S. S., Panwar, D., Lin, C., Sharma, T. K., Rastogi, D., Bindewari, S., Singh, A., Li, Y., Agarwal, N., & Agarwal, S. (2025). Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations [Review of Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations]. Journal Of Big Data, 12(1). Springer Science+Business Media. https://doi.org/10.1186/s40537-025-01201-x
Ghildayal, N., Nagavedu, K., Wiltz, J. L., Back, S., Boehmer, T. K., Draper, C., Gundlapalli, A. V., Horgan, C., Marsolo, K., Mazumder, N. R., Reynolds, J. S., Ritchey, M. D., Saydah, S., Tedla, Y. G., Carton, T. W., & Block, J. P. (2024). Public Health Surveillance in Electronic Health Records: Lessons From PCORnet. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.230417
Greenlund, K. J., Lu, H., Wang, Y., Matthews, K. A., LeClercq, J., Lee, B., & Carlson, S. A. (2022). PLACES: Local Data for Better Health. Preventing Chronic Disease, 19. https://doi.org/10.5888/pcd19.210459
Hacker, K., Briss, P. A., Richardson, L. C., Wright, J. S., & Petersen, R. (2021). COVID-19 and Chronic Disease: The Impact Now and in the Future. Preventing Chronic Disease, 18. https://doi.org/10.5888/pcd18.210086
Hacker, K., & Kaufmann, R. (2024). Chronic Disease Mapping, an Important Strategy and Tool for Health Promotion. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.240110
Hacker, K., Thomas, C. W., Zhao, G., Claxton, J. S., Eke, P. I., & Town, M. (2024). Social Determinants of Health and Health-Related Social Needs Among Adults With Chronic Diseases in the United States, Behavioral Risk Factor Surveillance System, 2022. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.240362
Heath, G. W., Levine, D. I., Oppong, G., & Alghader, M. (2025). Prevalence of post COVID-19 condition and associations with risk factors among U.S. adults: 2023 Behavioral Risk Factor Surveillance System. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1662273
Hohman, K. H., Klompas, M., Zambarano, B., Wall, H. K., Jackson, S. L., & Kraus, E. M. (2024). Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.230409
Hohman, K. H., Zambarano, B., Klompas, M., Wall, H. K., Kraus, E. M., Carton, T. W., & Jackson, S. L. (2023). Development of a Hypertension Electronic Phenotype for Chronic Disease Surveillance in Electronic Health Records: Key Analytic Decisions and Their Effects. Preventing Chronic Disease, 20. https://doi.org/10.5888/pcd20.230026
Hu, K., Li, C., Yang, X., Ou, S., Zhang, X., Xiao, D., & Yu, M. (2025). From infectious diseases to chronic diseases: the paradigm shift of spatial epidemiology in disease prevention and control. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1698964
Huston, S. L., & Porter, A. (2023). State and Local Health Departments: Research, Surveillance, and Evidence-Based Public Health Practices. Preventing Chronic Disease, 20. https://doi.org/10.5888/pcd20.230142
Igwama, G. T., Nwankwo, E. I., Emeihe, E. V., & Ajegbile, M. D. (2024). AI and big data analytics for enhancing public health surveillance in rural communities. International Journal of Applied Research in Social Sciences, 6(8), 1797. https://doi.org/10.51594/ijarss.v6i8.1427
Ijeh, S., Okolo, C. A., Arowoogun, J. O., Adeniyi, A. O., & Omotayo, O. (2024). PREDICTIVE MODELING FOR DISEASE OUTBREAKS: A REVIEW OF DATA SOURCES AND ACCURACY [Review of PREDICTIVE MODELING FOR DISEASE OUTBREAKS: A REVIEW OF DATA SOURCES AND ACCURACY]. International Medical Science Research Journal, 4(4), 406. Fair East Publishers. https://doi.org/10.51594/imsrj.v4i4.999
Jack, L. (2023). Preventing Chronic Disease in 2023: More Volunteers, New Appointments, Upcoming Collections, Acknowledgment of Guest Editorial Board on Racism, and Updates on Diversity, Equity, and Inclusion Initiatives. Preventing Chronic Disease, 20. https://doi.org/10.5888/pcd20.230131
Jackson, S. L., Lekiachvili, A., Block, J. P., Richards, T. B., Nagavedu, K., Draper, C., Koyama, A. K., Womack, L. S., Carton, T. W., Mayer, K. H., Rasmussen, S. A., Trick, W. E., Chrischilles, E. A., Weiner, M. G., Podila, P. S. B., Boehmer, T. K., Wiltz, J. L., & Partners, on behalf of Pcor. N. (2024). Preventive Service Usage and New Chronic Disease Diagnoses: Using PCORnet Data to Identify Emerging Trends, United States, 2018–2022. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.230415
Kenney, M., & Mamo, L. (2025). Precision public health after Covid-19: a scoping review [Review of Precision public health after Covid-19: a scoping review]. International Journal for Equity in Health, 24(1), 129. BioMed Central. https://doi.org/10.1186/s12939-025-02489-0
Khoury, M. J., & Holt, K. E. (2021). The impact of genomics on precision public health: beyond the pandemic. Genome Medicine, 13(1). https://doi.org/10.1186/s13073-021-00886-y
Kim, C., Rossen, L. M., Stierman, B., Garrison, V. H., Hales, C. M., & Ogden, C. L. (2023). Federal Housing Assistance and Chronic Disease Among US Adults, 2005–2018. Preventing Chronic Disease, 20. https://doi.org/10.5888/pcd20.230144
Li, P., Ma, L., Liu, J., & Zhang, L. (2022). Surveillance of Noncommunicable Diseases: Opportunities in the Era of Big Data. Health Data Science, 2022, 9893703. https://doi.org/10.34133/2022/9893703
Li, W., Zhang, M., Cheng, J., Jackson, S. L., Vaughan, A. S., Klompas, M., Carton, T. W., Nauman, E., Mendoza, L., Adhikari, A., Donovan, J., & Hohman, K. H. (2025). Weighted EHR-based prevalence estimates for hypertension at the state and local levels in Louisiana. BMC Public Health, 25(1). https://doi.org/10.1186/s12889-025-21633-7
Liu, Y., & Wang, B. (2025). Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review [Review of Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review]. Frontiers in Public Health, 13, 1510456. Frontiers Media. https://doi.org/10.3389/fpubh.2025.1510456
Maha, C. C., Kolawole, T. O., & Abdul, S. (2024). Harnessing data analytics: A new frontier in predicting and preventing non-communicable diseases in the US and Africa. Computer Science & IT Research Journal, 5(6), 1247. https://doi.org/10.51594/csitrj.v5i6.1196
Matus, P., Sepúlveda-Peñaloza, A., Page, K., Rodríguez, C., Cárcamo, M., Bustamante, F., Garrido, M., & Urquidi, C. (2024). The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform. Frontiers in Public Health, 12, 1407514. https://doi.org/10.3389/fpubh.2024.1407514
McAndrew, T., Lover, A. A., Hoyt, G., & Majumder, M. S. (2025). When data disappear: public health pays as US policy strays [Review of When data disappear: public health pays as US policy strays]. The Lancet Digital Health, 7(7), 100874. Elsevier BV. https://doi.org/10.1016/j.landig.2025.100874
Morgenstern, J., Rosella, L. C., Daley, M., Goel, V., Schünemann, H. J., & Piggott, T. (2021). “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC Public Health, 21(1). https://doi.org/10.1186/s12889-020-10030-x
Nansikombi, H. T., Kwesiga, B., Aceng, F. L., Ario, A. R., Bulage, L., & Arinaitwe, E. S. (2023). Timeliness and completeness of weekly surveillance data reporting on epidemic prone diseases in Uganda, 2020–2021. BMC Public Health, 23(1), 647. https://doi.org/10.1186/s12889-023-15534-w
Nguyen, T. N. T., Nguyen, T. T. T., Bao, T. Q., Pham, C. T., Perry, K. E., Haregu, T., Oldenburg, B., & Kowal, P. (2023). Putting non-communicable disease data to work in Vietnam: an investigation of community health surveillance capacity. BMC Public Health, 23(1). https://doi.org/10.1186/s12889-023-14986-4
Okoro, Y. O., Ayo-Farai, O., Maduka, C. P., Okongwu, C. C., & Sodamade, O. T. (2023). EMERGING TECHNOLOGIES IN PUBLIC HEALTH CAMPAIGNS: ARTIFICIAL INTELLIGENCE AND BIG DATA. Acta Informatica Malaysia, 8(1), 5. https://doi.org/10.26480/aim.01.2024.05.10
Olawade, D. B., Wada, O. J., David-Olawade, A. C., Kunonga, E., Abaire, O. J., & Ling, J. (2023). Using artificial intelligence to improve public health: a narrative review [Review of Using artificial intelligence to improve public health: a narrative review]. Frontiers in Public Health, 11. Frontiers Media. https://doi.org/10.3389/fpubh.2023.1196397
Rajendran, E. G., Hairi, F. M., Supramaniam, R. K., & Mohd, T. A. M. T. (2024). Precision public health, the key for future outbreak management: A scoping review [Review of Precision public health, the key for future outbreak management: A scoping review]. Digital Health, 10. SAGE Publishing. https://doi.org/10.1177/20552076241256877
Roberts, M. C., Fohner, A. E., Landry, L., Olstad, D. L., Smit, A. K., Turbitt, E., & Allen, C. G. (2021). Advancing precision public health using human genomics: examples from the field and future research opportunities. Genome Medicine, 13(1). https://doi.org/10.1186/s13073-021-00911-0
Roberts, M. C., Holt, K. E., Fiol, G. D., Baccarelli, A., & Allen, C. G. (2024). Precision public health in the era of genomics and big data [Review of Precision public health in the era of genomics and big data]. Nature Medicine, 30(7), 1865. Nature Portfolio. https://doi.org/10.1038/s41591-024-03098-0
Talias, Μ. A., Lamnisos, D., & Heraclides, A. (2022). Editorial: Data science and health economics in precision public health. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.960282
Thomas, S., Browning, C., Charchar, F. J., Klein, B., Ory, M. G., Bowden‐Jones, H., & Chamberlain, S. R. (2023). Transforming global approaches to chronic disease prevention and management across the lifespan: integrating genomics, behavior change, and digital health solutions [Review of Transforming global approaches to chronic disease prevention and management across the lifespan: integrating genomics, behavior change, and digital health solutions]. Frontiers in Public Health, 11. Frontiers Media. https://doi.org/10.3389/fpubh.2023.1248254
Velmovitsky, P. E., Bevilacqua, T., Alencar, P., Cowan, D., & Morita, P. P. (2021). Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective [Review of Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective]. Frontiers in Public Health, 9, 561873. Frontiers Media. https://doi.org/10.3389/fpubh.2021.561873
Wang, Y., Deng, R. A., & Geng, X. (2025). Exploring the integration of medical and preventive chronic disease health management in the context of big data [Review of Exploring the integration of medical and preventive chronic disease health management in the context of big data]. Frontiers in Public Health, 13, 1547392. Frontiers Media. https://doi.org/10.3389/fpubh.2025.1547392
Watson, K. B., Carlson, S. A., Lu, H., Wooten, K. G., Pankowska, M., & Greenlund, K. J. (2024). Chronic Disease Indicators: 2022–2024 Refresh and Modernization of the Web Tool. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.240109
Watson, K. B., Wiltz, J. L., Nhim, K., Kaufmann, R., Thomas, C. W., & Greenlund, K. J. (2025). Trends in Multiple Chronic Conditions Among US Adults, By Life Stage, Behavioral Risk Factor Surveillance System, 2013–2023. Preventing Chronic Disease, 22. https://doi.org/10.5888/pcd22.240539
Wiltz, J. L., Lee, B., Kaufmann, R., Carney, T. J., Davis, K., & Briss, P. A. (2024). Modernizing CDC’s Practices and Culture for Better Data Sharing, Impact, and Transparency. Preventing Chronic Disease, 21. https://doi.org/10.5888/pcd21.230200
GUIMARÃES, Mateus Henrique Dias. GLOBAL HEALTH RESPONSES TO REDUCE INEQUALITIES IN ADDRESSING HEALTH CRISES. Even3 Publicações, v. 10, p. 7763526, 2026. DOI http://doi.org/10.29327/7763526. Disponível em: https://publicacoes.even3.com.br/preprint/global-health-responses-to-reduce-inequalities-in-addressing-health-crises-7635267
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