1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Public Health
  4. Volume 38, 2017
  5. Article

Annual Review of Public Health

Volume 38, 2017

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

Abstract

Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics.

Keyword(s): public health surveillance, public health surveillance systems, surveillance evaluation, surveillance indicators, surveillance objectives, surveillance system monitoring
Loading

Article metrics loading...

/content/journals/10.1146/annurev-publhealth-031816-044348
2017-03-20
2024-06-02
Loading full text...

Full text loading...

/deliver/fulltext/publhealth/38/1/annurev-publhealth-031816-044348.html?itemId=/content/journals/10.1146/annurev-publhealth-031816-044348&mimeType=html&fmt=ahah

Literature Cited

  1. Althouse BM, Ng YY, Cummings DAT. 1.  2011. Prediction of dengue incidence using search query surveillance. PLOS Negl. Trop. Dis. 5e1258 [Google Scholar]
  2. Althouse BM, Scarpino SV, Meyers LA, Ayers JW, Bargsten M. 2.  et al. 2015. Enhancing disease surveillance with novel data streams: challenges and opportunities. EPJ Data Sci. 4: 17 https://doi.org/10.1140/epjds/s13688-015-0054-0 [Google Scholar]
  3. Anderson BD, Ma M, Xia Y, Wang T, Shu B. 3.  et al. 2016. Bioaerosol sampling in modern agriculture: a novel approach for emerging pathogen surveillance?. J. Infect. Dis. 214:537–45 https://doi.org/10.1093/infdis/jiw180 [Google Scholar]
  4. Bajardi P, Vespignani A, Funk S, Eames KT, Edmunds WJ. 4.  et al. 2014. Determinants of follow-up participation in the Internet-based European influenza surveillance platform Influenzanet. J. Med. Internet Res. 16:e78 https://doi.org/10.2196/jmir.3010 [Google Scholar]
  5. Baker MA, Nguyen M, Cole DV, Lee GM, Lieu TA. 5.  2013. Post-licensure rapid immunization safety monitoring program (PRISM) data characterization. Vaccine 31Suppl.K98–112 [Google Scholar]
  6. Bednarczyk RA, Orenstein WA, Omer SB. 6.  2016. Estimating the number of measles-susceptible children and adolescents in the United States using data from the National Immunization Survey-Teen (NIS-Teen). Am. J. Epidemiol. 184:148–56 https://doi.org/10.1093/aje/kwv320 [Google Scholar]
  7. Bernstein JA, Friedman C, Jacobson P, Rubin JC. 7.  2015. Ensuring public health's future in a national-scale learning health system. Am. J. Prev. Med. 48: 480–87 [Google Scholar]
  8. Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC. 8.  et al. 2016. Results from the Centers for Disease Control and Prevention's Predict the 2013–2014 Influenza Season Challenge. BMC Infect. Dis. 16357 https://doi.org/10.1186/s12879-016-1669-x [Google Scholar]
  9. Birkhead GS, Klompas M, Shah NR. 9.  2015. Uses of electronic health records for public health surveillance to advance public health. Annu. Rev. Public Health 36: 345–59 [Google Scholar]
  10. Blondin KJ, Giles CM, Cradock AL, Gortmaker SL, Long MW. 10.  2016. US state childhood obesity surveillance practices and recommendations for improving them, 2014–2015. Prev. Chronic Dis. 13160060 https://doi.org/10.5888/pcd13.160060 [Google Scholar]
  11. Bowen MD, Mijatovic-Rustempasic S, Esona MD, Teel EN, Gautam R. 11.  et al. 2016. Rotavirus strain trends during the postlicensure vaccine era: United States, 2008–2013. J. Infect. Dis. 214:732–38 https://doi.org/10.1093/infdis/jiw233 [Google Scholar]
  12. Broniatowski DA, Paul MJ, Dredze M. 12.  2013. National and local influenza surveillance through Twitter: an analysis of the 2012–2013 influenza epidemic. PLOS ONE 8e83672 https://doi.org/10.1371/journal.pone.0083672 [Google Scholar]
  13. Brownson RC, Fielding JE, Maylahn CM. 13.  2009. Evidence-based public health: a fundamental concept for public health practice. Annu. Rev. Public Health 30175–201 [Google Scholar]
  14. Buckeridge DL. 14.  2007. Outbreak detection through automated surveillance: a review of the determinants of detection. J. Biomed. Inform. 40370–79 [Google Scholar]
  15. Buckeridge DL, Burkom H, Campbell M, Hogan WR, Moore AW. 15.  2005. Algorithms for rapid outbreak detection: a research synthesis. J. Biomed. Inform. 3899–113 [Google Scholar]
  16. Buckeridge DL, Charland K, Labban A, Ma Y. 16.  2014. A method for neighborhood-level surveillance of food purchasing. Ann. N. Y. Acad. Sci. 1331:270–77 https://doi.org/10.1111/nyas.12332 [Google Scholar]
  17. Buckeridge DL, Owens DK, Switzer P, Frank J, Musen MA. 17.  2006. Evaluating detection of an inhalational anthrax outbreak. Emerg. Infect. Dis. 12:1942–49 [Google Scholar]
  18. Burris S, Hitchcock L, Ibrahim J, Penn M, Ramanathan T. 18.  2016. Policy surveillance: a vital public health practice comes of age. J. Health Polit. Policy Law. In press https://doi.org/10.1215/03616878–3665931 [Google Scholar]
  19. Cadieux G, Buckeridge DL, Jacques A, Libman M, Dendukuri N, Tamblyn R. 19.  2012. Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions. BMC Public Health 12166 [Google Scholar]
  20. Carroll LN, Au AP, Detwiler LT, Fu T, Painter IS, Abernethy NF. 20.  2014. Visualization and analytics tools for infectious disease epidemiology: a systemic review. J. Biomed. Inform. 51287–98 [Google Scholar]
  21. 21. CDC (Cent. Dis. Control Prev.) 2001. Updated guidelines for evaluating public health surveillance systems: recommendations from the guidelines working group. Morb. Mortal. Wkly. Rep. Recomm. Rep. 50RR-131–35 [Google Scholar]
  22. Chapman WW, Dowling JN, Wagner MM. 22.  2005. Classification of emergency department chief complaints into 7 syndromes: a retrospective analysis of 527,228 patients. Ann. Emerg. Med. 46445–55 [Google Scholar]
  23. Chriqui JF, O'Connor JC, Chaloupka FJ. 23.  2011. What gets measured, gets changed: evaluating law and policy for maximum impact. J. Law Med. Ethics 39Suppl. 121–26 [Google Scholar]
  24. Condell O, Midgley S, Christiansen CB, Chen M, Chen Nielsen X. 24.  et al. 2016. Evaluation of the enterovirus laboratory surveillance system in Denmark, 2010 to 2013. Euro Surveill 21:1830218 https://doi.org/10.2807/1560–7917.ES.2016.21.18.30218 [Google Scholar]
  25. Doyle TJ, Ma H, Groseclose SL, Hopkins RS. 25.  2005. PHSkb: a knowledgebase to support notifiable disease surveillance. BMC Med. Inform. Decis. Mak. 5:27 [Google Scholar]
  26. Drewe JA, Hoinville LJ, Cook AJC, Floyd T, Stark KDC. 26.  2012. Evaluation of animal and public health surveillance systems: a systematic review. Epidemiol. Infect. 140:575–90 [Google Scholar]
  27. 27. Eur. Cent. Dis. Prev. Control 2014. Data Quality Monitoring and Surveillance System Evaluation: A Handbook of Methods and Applications. Stockholm, Swed.: Eur. Cent. Dis. Prev. Control http://ecdc.europa.eu/en/publications/publications/data-quality-monitoring-surveillance-system-evaluation-sept-2014.pdf [Google Scholar]
  28. Foege WH, Hogan RC, Newton LH. 28.  1976. Surveillance projects for selected diseases. Int. J. Epidemiol. 5129–37 [Google Scholar]
  29. Freifeld CC, Mandl KD, Reis BY, Brownstein JS. 29.  2008. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J. Am. Med. Inform. Assoc. 15:150–57 [Google Scholar]
  30. Friede A, Blum HL, McDonald M. 30.  1995. Public health informatics: how information-age technology can strengthen public health. Annu. Rev. Public Health 16:239–52 [Google Scholar]
  31. Groseclose SL, German RR, Nsubuga P. 31.  2010. Evaluating public health surveillance. Principles and Practice of Public Health Surveillance LM Lee, SM Teutsch, ME St. Louis, SB Thacker 166–97. New York: Oxford Univ. Press, 3rd ed.. [Google Scholar]
  32. Hall HI, Mokotoff ED. 32.  2007. Setting standards and an evaluation framework for human immunodeficiency virus/acquired immunodeficiency syndrome surveillance. J. Public Health Manag. Pract. 13:519–23 [Google Scholar]
  33. Hall HI, Song R, Gerstle JE III, Lee LM. 33.  2006. Assessing the completeness of reporting of human immunodeficiency virus diagnoses in 2002–2003: capture-recapture methods. Am. J. Epidemiol. 164:391–97 [Google Scholar]
  34. Harris JK, Mansour R, Choucair B, Olson J, Nissen C, Bhatt J. 34.  2014. Health department use of social media to identify foodborne illness—Chicago, Illinois, 2013–2014. Morb. Mortal. Wkly. Rep 63681–85 [Google Scholar]
  35. Harrison C, Jorder M, Stern H, Stavinsky F, Reddy V. 35.  et al. 2014. Using online reviews by restaurant patrons to identify unreported cases of foodborne illness—New York City, 2012–2013. Morb. Mortal. Wkly. Rep 63441–45 [Google Scholar]
  36. Hemming K, Lilford R, Girling AJ. 36.  2015. Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level designs. Stat. Med. 34181–96 https://doi.org/10.1002/sim.6325 [Google Scholar]
  37. Hopkins RS. 37.  2005. Design and operation of state and local infectious disease surveillance systems. J. Public Health Manag. Pract. 11:184–90 [Google Scholar]
  38. Hulth A, Andrews N, Ethelberg S, Dreesman J, Faensen D. 38.  et al. 2010. Practical usage of computer-supported outbreak detection in five European countries. Euro Surveill 153619658 [Google Scholar]
  39. 39. Inst. Med 1988. The Future of Public Health. Washington, DC: Natl. Acad. Press doi:10.17226/1091 [Google Scholar]
  40. Jim MA, Arias E, Seneca DS, Hoopes MJ, Jim CC. 40.  et al. 2014. Racial misclassification of American Indians and Alaska Natives by Indian Health Service contract health service delivery area. Am. J. Public Health 104:S295–302 https://doi.org/10.2105/AJPH.2014.301933 [Google Scholar]
  41. Jorm L, Gruszin S, Churches T. 41.  2009. A multidimensional classification of public health activity in Australia. Aust. N. Z. Health Policy 69 https://doi.org/10.1186/1743-8462-6-9 [Google Scholar]
  42. Karch DL, Chen M, Tang T. 42.  2014. Evaluation of the National Human Immunodeficiency Virus Surveillance System for the 2011 diagnosis year. J. Public Health Manag. Pract. 20:598–607 [Google Scholar]
  43. Kass-Hout TA, Xu Z, Mohebbi M, Nelsen H, Baker A. 43.  et al. 2016. OpenFDA: an innovative platform providing access to a wealth of FDA's publicly available data. J. Am. Med. Inform. Assoc 23:596–600 https://doi.org/10.1093/jamia/ocv153 [Google Scholar]
  44. Kempf AM, Remington PL. 44.  2007. New challenges for telephone survey research in the twenty-first century. Annu. Rev. Public Health 28:113–26 [Google Scholar]
  45. Kirkcaldy RD, Harvey A, Papp JR, del Rio C, Soge OO. 45.  et al. 2016. Neisseria gonorrhoeae antimicrobial susceptibility surveillance—the Gonococcal Isolate Surveillance Project, 27 sites, United States, 2014. Morb. Mortal. Wkly. Rep. Surveill. Summ. 65:71–19 [Google Scholar]
  46. Kleinman KP, Abrams AM. 46.  2008. Assessing the utility of public health surveillance using specificity, sensitivity, and lives saved. Stat. Med. 27:4057–68 [Google Scholar]
  47. Kukafka R, Yasnoff WA. 47.  2007. Public health informatics. J. Biomed. Inform. 40365–69 [Google Scholar]
  48. Langley G, Schaffner W, Farley MM, Lynfield R, Bennett NM. 48.  et al. 2015. Twenty years of Active Bacterial Core surveillance. Emerg. Infect. Dis. 21:1520–28 [Google Scholar]
  49. Lazarus R, Klompas M, Campion FX, NcNabb SJN, Hou X. 49.  et al. 2009. Electronic support for public health: validated case finding and reporting for notifiable diseases using electronic medical data. J. Am. Med. Inform. Assoc. 16:18–24 https://doi.org/10.1197/jamia.M2848.a [Google Scholar]
  50. Lewis B, Eubank S, Abrams AM, Kleinman K. 50.  2013. In silico surveillance: evaluating outbreak detection with simulation models. BMC Med. Inform. Decis. Mak. 1312 [Google Scholar]
  51. Lobb R, Colditz GA. 51.  2013. Implementation science and its application to population health. Annu. Rev. Public Health 34235–51 [Google Scholar]
  52. Ma H, Rolka H, Mandl K, Buckeridge D, Fleischauer A, Pavlin J. 52.  2005. Implementation of laboratory order data in BioSense Early Event Detection and Situation Awareness System. Morb. Mortal. Wkly. Rep 54Suppl.27–30 [Google Scholar]
  53. Makam AN, Nguyen OK, Moore B, Ma Y, Amarasingham R. 53.  2013. Identifying patients with diabetes and the earliest data of diagnosis in real time: an electronic health record case-finding algorithm. BMC Med. Inform. Decis. Mak. 1381 [Google Scholar]
  54. McNabb SJN, Surdo AM, Redmond A, Cobb J, Wiley J. 54.  et al. 2004. Applying a new conceptual framework to evaluate tuberculosis surveillance and action performance and measure the costs, Hillsborough County, Florida, 2002. Ann. Epidemiol 14:640–45 [Google Scholar]
  55. Mokdad AH. 55.  2009. The Behavioral Risk Factor Surveillance System: past, present, and future. Annu. Rev. Public Health 3043–54 [Google Scholar]
  56. Morrison KT, Shaddick G, Henderson SB, Buckeridge DL. 56.  2016. A latent process model for forecasting multiple time series in environmental public health surveillance. Stat. Med. 353085–100 [Google Scholar]
  57. Myslín M, Zhu SH, Chapman W, Conway M. 57.  2013. Using Twitter to examine smoking behavior and perceptions of emerging tobacco products. J. Med. Internet Res. 158e174 [Google Scholar]
  58. Nsoesie EO, Buckeridge DL, Brownstein JS. 58.  2014. Guess who's not coming to dinner? Evaluating online restaurant reservations for disease surveillance. J. Med. Internet Res. 161e22 [Google Scholar]
  59. Okhmatovskaia A, Shaban-Nejad A, Lavigne M, Buckeridge DL. 59.  2014. Addressing the challenge of encoding causal epidemiological knowledge in formal ontologies: a practical perspective. Stud. Health Technol. Inform 205:1125–29 [Google Scholar]
  60. Osborne JD, Wyatt M, Westfall AO, Willig J, Bethard S, Gordon G. 60.  2016. Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning. J. Am. Med. Inform. Assoc In press https://doi.org/10.1093/jamia/ocw006 [Google Scholar]
  61. Parton HB, Mathes R, Abdelnabi J, Alleyne L, Econome A. 61.  et al. 2016. Investigating a syndromic surveillance signal with complementary data systems. Online J. Public Health Inform. 81e152 [Google Scholar]
  62. Patwardhan A, Bilkovski R. 62.  2012. Comparison: flu prescription sales data from a retail pharmacy in the US with Google flu trends and US ILINet (CDC) data as flu activity indicator. PLOS ONE 78e43611 [Google Scholar]
  63. Pinner RW, Lynfield R, Hadler JL, Schaffner W, Farley MM. 63.  et al. 2015. Cultivation of an adaptive domestic network for surveillance and evaluation of emerging infections. Emerg. Infect. Dis. 21:1499–509 [Google Scholar]
  64. Pollack KM, D'Angelo J, Green G, Conte S, Fealy S. 64.  et al. 2016. Developing and implementing Major League Baseball's health and injury tracking system. Am. J. Epidemiol. 183:490–96 [Google Scholar]
  65. Powell GE, Seifert HA, Reblin T, Burstein PJ, Blowers J. 65.  et al. 2016. Social media listening for routine post-marketing safety surveillance. Drug Saf 39443–54 [Google Scholar]
  66. Presley D, Reinstein T, Webb-Barr D, Burris S. 66.  2015. Creating legal data for public health monitoring and evaluation: Delphi standards for policy surveillance. J. Law Med. Ethics 4327–31 [Google Scholar]
  67. Prue CE, Lackey C, Swenarski L, Gantt JM. 67.  2003. Communication monitoring: shaping CDC's emergency risk communication efforts. J. Health Commun. 8Suppl. 135–49 [Google Scholar]
  68. Puckett M, Neri A, Rohan E, Clerkin C, Underwood JM. 68.  et al. 2016. Evaluating early case capture of pediatric cancers in seven central cancer registries in the United States, 2013. Public Health Rep 131:126–36 [Google Scholar]
  69. Roush SW. 69.  2011. Surveillance indicators. Manual for the Surveillance of Vaccine-Preventable Diseases Cent. Dis. Control Prev . Atlanta , GA: Cent. Dis. Control Prev, 5th ed. http://www.cdc.gov/vaccines/pubs/surv-manual/chpt18-surv-indicators.pdf [Google Scholar]
  70. Sabbe M, Berger N, Blommaert A, Ogunjimi B, Grammens T. 70.  et al. 2016. Sustained low rotavirus activity and hospitalization rates in the post-vaccination era in Belgium, 2007 to 2014. Euro Surveill 212730273 [Google Scholar]
  71. Salathe M, Friefeld CC, Mekaru SR, Tomasulo AF, Brownstein JS. 71.  2013. Influenza A (H7N9) and the importance of digital epidemiology. N. Engl. J. Med. 369401–4 [Google Scholar]
  72. Salmon D, Yih WK, Lee G, Rosofsky R, Brown J. 72.  et al. 2012. Success of program linking data sources to monitor H1N1 vaccine safety points to potential for even broader safety surveillance. Health Aff 312518–27 [Google Scholar]
  73. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. 73.  2015. Combining search, social media, and traditional data sources to improve influenza surveillance. PLOS Comput. Biol. 11e1004513 [Google Scholar]
  74. Savard N, Bédard L, Allard R, Buckeridge DL. 74.  2015. Using age, triage score, and disposition data from emergency department electronic records to improve Influenza-like illness surveillance. J. Am. Med. Inform. Assoc. 22:688–96 [Google Scholar]
  75. Shaban-Nejad A, Lavigne M, Okhmatovskaia A, Buckeridge DL. 75.  2016. PopHR: a knowledge-based platform to support integration, analysis and visualization of population health data. Ann. N. Y. Acad. Sci. In press [Google Scholar]
  76. Signorini A, Segre AM, Polgreen PM. 76.  2011. The use of Twitter to track levels of disease activity and public concern in the U.S. during the Influenza A H1N1 pandemic. PLOS ONE 6e19467 [Google Scholar]
  77. Skoog G, Struwe J, Cars O, Hangerger H, Odenholt I. 77.  et al. 2016. Repeated nationwide point-prevalence surveys for antimicrobial use in Swedish hospitals: data for actions 2003–2010. Euro Surveill 21:30264 [Google Scholar]
  78. Smith GE, Elliot AJ, Ibbotson S, Morbey R, Edeghere O. 78.  et al. 2016. Novel public health risk assessment process developed to support syndromic surveillance for the 2012 Olympic and Paralympic Games. J. Public Health. In press https://doi.org/10.1093/pubmed/fdw054 [Google Scholar]
  79. Smith PF, Hadler JL, Stanbury M, Rolfs RT, Hopkins RS. 79.  2013. “Blueprint Version 2.0”: updating public health surveillance for the 21st century. J. Public Health Manag. Pract. 19:231–39 [Google Scholar]
  80. Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM. 80.  et al. 2015. Flu Near You: crowdsourced symptom reporting spanning 2 influenza seasons. Am. J. Public Health 105:2124–30 [Google Scholar]
  81. Snider CJ, Diop OM, Tangermann RH, Wassilek SGF. 81.  2016. Surveillance systems to track progress toward polio eradication—worldwide, 2014–2015. Morb. Mortal. Wkly. Rep. 65: 346–51 [Google Scholar]
  82. Somda ZC, Perry HN, Messonnier NR, Djingarey MH, Ki SO, Meltzer MI. 82.  2010. Modeling the cost-effectiveness of the Integrated Disease Surveillance and Response (ISDR) system: meningitis in Burkina Faso. PLOS ONE 59e13044 [Google Scholar]
  83. Tebbens RJD, Sangrujee N, Thompson KM. 83.  2006. The costs of future polio risk management policies. Risk Anal 26:1507–31 [Google Scholar]
  84. Thacker SB, Berkelman RL. 84.  1988. Public health surveillance in the United States. Epidemiol. Rev. 10:164–90 [Google Scholar]
  85. Thacker SB, Berkelman RL, Stroup DF. 85.  1989. The science of public health surveillance. J. Public Health Policy 10:187–203 [Google Scholar]
  86. Thoroughman DA, Frederickson D, Cameron HD, Shelby LK, Cheek JE. 86.  2002. Racial misclassification of American Indians in Oklahoma state surveillance data for sexually transmitted diseases. Am. J. Epidemiol. 155:1137–41 [Google Scholar]
  87. van Noort SP, Codeco CT, Koppeschaar CE, van Ranst M, Paolotti D, Gomes MG. 87.  2015. Ten-year performance of Influenzanet: ILI time series, risks, vaccine effects, and care-seeking behaviour. Epidemics 13:28–36 [Google Scholar]
  88. Wong WK, Moore A, Cooper G, Wagner MM. 88.  2003. WSARE: What's Strange About Recent Events?. J. Urban Health 80Suppl. 1i66–75 [Google Scholar]
  89. Yasnoff WA, O'Carroll PW, Koo D, Linkins RW, Kilbourne EM. 89.  2000. Public health informatics: improving and transforming public health in the information age. J. Public Health Manag. Pract. 667–75 [Google Scholar]
  90. Zinszer K, Jauvin C, Verma A, Bedard L, Allard R. 90.  et al. 2010. Residential address errors in public health surveillance data: a description and analysis of the impact on geocoding. Spat. Spatiotemporal Epidemiol. 1:2–3163–68 [Google Scholar]
/content/journals/10.1146/annurev-publhealth-031816-044348
Loading
Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation
Annual Review of Public Health 38, 57 (2017); https://doi.org/10.1146/annurev-publhealth-031816-044348
/content/journals/10.1146/annurev-publhealth-031816-044348
/content/journals/10.1146/annurev-publhealth-031816-044348
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error