State & local chronic disease surveillance using electronic health record systems

American Journal of Public Health

Volumn 107, Issue 9, 2017, Pages 1406-1412

  Klompas, Michael ^a   Cocoros, Noelle M ^a   Menchaca, John T ^a   Erani, Diana ^b   Hafer, Ellen ^b   Herrick, Brian ^c   Josephson, Mark ^b   Lee, Michael ^d   Weiss, Michelle D Payne ^c   Zambarano, Bob ^e   Eberhardt, Karen R ^e   Malenfant, Jessica ^a   Nasuti, Laura ^f   Land, Thomas ^f  

^a HARVARD MEDICAL SCHOOL   (United States)

^b Massachusetts League of Community Health Centers   (United States)

^c CAMBRIDGE HEALTH ALLIANCE   (United States)

^d Atrius Health   (United States)

^e Commonwealth Informatics Inc ^*  (United States)

^f MASSACHUSETTS DEPARTMENT OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM; CHRONIC DISEASE; ELECTRONIC HEALTH RECORD; HEALTH BEHAVIOR; HUMAN; MASSACHUSETTS; MIDDLE AGED; PREVALENCE; STATISTICS AND NUMERICAL DATA;

ADULT; BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM; CHRONIC DISEASE; ELECTRONIC HEALTH RECORDS; HEALTH BEHAVIOR; HUMANS; MASSACHUSETTS; MIDDLE AGED; PREVALENCE;

EID: 85027884075     PISSN: 00900036     EISSN: 15410048     Source Type: Journal    
DOI: 10.2105/AJPH.2017.303874     Document Type: Article

References (31)

1. Clark CR, Williams DR. Understanding county-level, cause-specificmortality: The great value-And limitations- of small area data. JAMA. 2016;316(22):2363-2365.
2. Johnson NB, Hayes LD, Brown K, Hoo EC, Ethier KA, Centers for Disease Control and Prevention. CDC National Health Report: leading causes of morbidity and mortality and associated behavioral risk and protective factors-United States, 2005-2013.MMWR Suppl. 2014; 63(4):3-27.
3. Chowdhury PP, Mawokomatanda T, Xu F, et al. Surveillance for certain health behaviors, chronic diseases, and conditions, access to health care, and use of preventive health services among states and selected local areas- Behavioral Risk Factor Surveillance System, United States, 2012. MMWR Surveill Summ. 2016;65(4):1-142.
4. Guo F, Garvey WT. Trends in cardiovascular health metrics in obese adults: National Health and Nutrition Examination Survey (NHANES), 1988-2014. J AmHeart Assoc. 2016;5(7):e003619.
5. Mokdad AH. The Behavioral Risk Factors Surveillance System: past, present, and future. Annu Rev Public Health. 2009;30:43-54.
6. Institute of Medicine. Existing surveillance data sources and systems. In: A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases.Washington, DC: National Academies Press; 2011:65-90.
7. Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004- 2011. BMC Med Res Methodol. 2013;13:49.
8. Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10):1021-1029.
9. Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in obesity among adults in the United States, 2005 to 2014. JAMA. 2016;315(21):2284-2291.
10. Klompas M, McVetta J, Lazarus R, et al. Integrating clinical practice and public health surveillance using electronic medical record systems. Am J Public Health. 2012;102(suppl 3):S325-S332.
11. Vogel J, Brown JS, Land T, Platt R, Klompas M. MDPHnet: secure, distributed sharing of electronic health record data for public health surveillance, evaluation, and planning. Am J Public Health. 2014;104(12):2265-2270.
12. Curtis LH, Brown J, Platt R. Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Aff (Millwood). 2014; 33(7):1178-1186.
13. Eggleston EM, Weitzman ER. Innovative uses of electronic health records and social media for public health surveillance. Curr Diab Rep. 2014;14(3):468.
14. Birkhead GS, Klompas M, Shah NR.Uses of electronic health records for public health surveillance to advance public health. Annu Rev Public Health. 2015;36:345-359.
15. Newton-Dame R, McVeigh KH, Schreibstein L, et al. Design of the New York City Macroscope: innovations in population health surveillance using electronic health records. EGEMS (Wash DC). 2016;4(1):1265.
16. McVeigh KH, Newton-Dame R, Chan PY, et al. Can electronic health records be used for population health surveillance? Validating population health metrics against established survey data.EGEMS(Wash DC). 2016;4(1):1267.
17. Romo ML, Chan PY, Lurie-Moroni E, et al. Characterizing adults receiving primary medical care in New York City: implications for using electronic health records for chronic disease surveillance. Prev Chronic Dis. 2016;13:E56.
18. Lazarus R, Klompas M, Campion FX, et al. Electronic support for public health: validated case finding and reporting for notifiable diseases using electronic medical data. J Am Med Inform Assoc. 2009;16(1):18-24.
19. Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R. Distributed health data networks: A practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. Med Care. 2010;48(6 suppl):S45-S51.
20. Klann JG, Buck MD, Brown J, et al. Query Health: standards-based, cross-platform population health surveillance. J Am Med Inform Assoc. 2014; 21(4):650-656.
21. Klompas M, Eggleston E, McVetta J, Lazarus R, Li L, Platt R. Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care. 2013;36(4):914-921.
22. Centers for Disease Control and Prevention. 500 Cities: local data for better health. 2017. Available at: https://www.cdc.gov/500cities/index.htm. Accessed January 16, 2017.
23. Zhang X, Holt JB, Lu H, et al. Multilevel regression and poststratification for small-Area estimation of population health outcomes: A case study of chronic obstructive pulmonary disease prevalence using the behavioral risk factor surveillance system. Am J Epidemiol. 2014;179(8):1025-1033.
24. Zhang X, Holt JB, Yun S, Lu H, Greenlund KJ, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system. Am J Epidemiol. 2015;182(2):127-137.
25. Connor Gorber S, Tremblay M, Moher D, Gorber B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: A systematic review. Obes Rev. 2007;8(4):307-326.
26. Holm M, Omenaas E, Gislason T, et al. Remission of asthma: A prospective longitudinal study from northern Europe (RHINE study). Eur Respir J. 2007;30(1):62-65.
27. Aaron SD, Vandemheen KL, FitzGerald JM, et al. Reevaluation of diagnosis in adults with physiciandiagnosed asthma. JAMA. 2017;317(3):269-279.
28. Linder JA, Rigotti NA, Brawarsky P, et al. Use of practice-based research network data to measure neighborhood smoking prevalence. Prev Chronic Dis. 2013;10: E84.
29. Davidson AJ, McCormick EV, Dickinson LM, Haemer MA, Knierim SD, Hambidge SJ. Populationlevel obesity surveillance: monitoring childhood body mass index z-score in a safety-net system. Acad Pediatr. 2014;14(6):632-638.
30. Leong A, Dasgupta K, Chiasson JL, Rahme E. Estimating the population prevalence of diagnosed and undiagnosed diabetes. Diabetes Care. 2013;36(10): 3002-3008.
31. Holt TA, Gunnarsson CL, Cload PA, Ross SD. Identification of undiagnosed diabetes and quality of diabetes care in the United States: cross-sectional study of 11.5 million primary care electronic records. CMAJ Open. 2014;2(4):E248-E255.