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Public health surveillance

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332:), and were used to monitor the quality of care at the patient, practice, and population levels. Since the data contained each patient's name and address, the system was also used to communicate directly with patients when the laboratory data indicated the need for attention. Out of control test results generated a letter to the patient suggesting they take action with their medical provider. Tests that were overdue generated reminders to have testing performed. The system also generated reminders and alerts with guideline-based advice for the practice as well as a periodic roster of each provider's patients and a report card summarizing the health status of the population. Clinical and economic evaluations of the system, including a large randomized 305:, are supposed to be routinely managed with frequent laboratory measurements. Since many laboratory results, at least in Europe and the US, are automatically processed by computerized laboratory information systems, the results are relatively easy to inexpensively collate in special purpose databases or disease registries. Unlike most syndromic surveillance systems, in which each record is assumed to be independent of the others, laboratory data in chronic conditions can be theoretically linked together at the individual patient level. If patient identifiers can be matched, a chronological record of each patient's laboratory results can be analyzed as well as aggregated to the population level. 255:, which was first released in 2008. Knowledge has also been used, though it is potentially prone to "noise", as it is a popular source of health information whether a user is ill or not. During the COVID-19 pandemic a new methodology has been developed to model COVID-19 prevalence based on web search activity. This methodology has also been used by Public Health England in the United Kingdom as one of their syndromic surveillance endpoints. 218:, closely matched CDC data, and led it by 1–2 weeks. However, it has been shown that the original approach behind Google Flu Trends had various modelling deficiencies leading to significant errors in its estimates. More recently, a series of more advanced linear and nonlinear approaches to influenza modeling from Google search queries have been proposed. Extending Google's work researchers from the Intelligent Systems Laboratory ( 2650: 152:. According to a CDC definition, "the term 'syndromic surveillance' applies to surveillance using health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response. Though historically syndromic surveillance has been utilized to target investigation of potential cases, its utility for detecting outbreaks associated with 243:
Influenzanet. However the range of potential data sources suitable for disease surveillance has increased as different areas have become digitized; today school attendance records, hospital emergency admissions data and even sales data, can be used for syndromic surveillance purposes. Search trends provide indirect data on public health, while the latter two methods provide direct data.
336:, demonstrated improvements in adherence to practice guidelines and reductions in the need for emergency department and hospital services as well as total costs per patient. The system has been commercialized and distributed to physicians, insurers, employers and others responsible for the care of chronically ill patients. It is now being expanded to other conditions such as 2674: 2662: 58:
providers and medical records are reviewed in order to identify a specific disease or condition. Passive surveillance systems are less time-consuming and less expensive to run but risk under-reporting of some diseases. Active surveillance systems are most appropriate for epidemics or where a disease has been targeted for elimination.
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Laboratory surveillance differs from population-wide surveillance because it can only monitor patients who are already receiving medical treatment and therefore having lab tests done. For this reason, it does not identify patients who have never been tested. Therefore, it is more suitable for quality
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has linked additional patient services to the registry such as health information and improved access to health care services. As of early 2012, the registry contains over 10 million test results on 3.6 million individuals. Although intended to improve health outcomes and reduce the incidence of the
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attack could save many lives and potentially stop or slow the spread of the outbreak. The most effective syndromic surveillance systems automatically monitor these systems in real-time, do not require individuals to enter separate information (secondary data entry), include advanced analytical tools,
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practice." Public health surveillance may be used to track emerging health-related issues at an early stage and find active solutions in a timely manner. Surveillance systems are generally called upon to provide information regarding when and where health problems are occurring and who is affected.
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Examples of social media public health surveillance include HealthTweets, which gathers data from Twitter. Twitter data is considered highly useful for public health research, as its data policies allow public access to 1% samples of raw tweets. Tweets can also be geolocated, which can be used to
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Digital surveillance of public health largely relies on a number of methods. The most important ones being the use of search-based trends on sites like Google and Knowledge, social media posts on platforms like Facebook and Twitter, and participatory surveillance websites such as Flu Near You and
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outbreak as an example, once the outbreak begins to affect the population, some people may call in sick for work/school, others may visit their drug store and purchase medicine over the counter, others will visit their doctor's office and other's may have symptoms severe enough that they call the
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Public health surveillance systems can be passive or active. A passive surveillance system consists of the regular, ongoing reporting of diseases and conditions by all health facilities in a given territory. An active surveillance system is one where health facilities are visited and health care
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Systems that can automate the process of identifying adverse drug events, are currently being used, and are being compared to traditional written reports of such events. These systems intersect with the field of medical informatics, and are rapidly becoming adopted by hospitals and endorsed by
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are two examples of crowd-sourced digital surveillance systems. Both sites recruit users to participate in surveys about influenza symptoms. Influenzanet was established in 2009, and operates in ten countries in Europe. Its predecessor was Grote Griepmeting, which was a Dutch/Belgian platform
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Syndromic surveillance systems monitor data from school absenteeism logs, emergency call systems, hospitals' over-the-counter drug sale records, Internet searches, and other data sources to detect unusual patterns. When a spike in activity is seen in any of the monitored systems disease
363:. Authorized by the Texas legislature and the state Health Department, the San Antonio Metropolitan Health District implemented the registry which drew results from all the major clinical laboratories in San Antonio. The program was discontinued in 2010 due to lack of funds. 312:-funded program called the Vermedx Diabetes Information System maintained a registry of laboratory values of diabetic adults in Vermont and northern New York State in the US with several years of laboratory results on thousands of patients. The data included measures of 2460: 268:, Facebook used aggregated, anonymized data collected from its platforms to provide human movement information to disease models. It also offered users a chance to participate in a disease symptom survey through 348: 2455: 2445: 308:
Laboratory registries allow for the analysis of the incidence and prevalence of the target condition as well as trends in the level of control. For instance, an
49:(WHO), "the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of 2495: 70: 1202: 347:, although unlike the Vermont Diabetes Information System, there are no provisions for patients to have their data excluded from the NYC database. The 462: 2528: 2500: 1540: 131:
in the United States). Issues in regard to healthcare improvement are evolving around the surveillance of medication errors within institutions.
2505: 1821: 2563: 2470: 1430: 1091:"Evaluating Google, Twitter, and Knowledge as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis" 2553: 2510: 2074: 1662: 285:
launched in 2003 and 2004. Flu Near You is used in the US. Another example of a surveillance sites is Dengue na Web, used to survey for
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White, Mary C.; Babcock, Frances; Hayes, Nikki S.; Mariotto, Angela B.; Wong, Faye L.; Kohler, Betsy A.; Weir, Hannah K. (2017-12-15).
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model the spread of contagious disease. It is the most used social media platform for public health surveillance. During the
1383:"The effect of the Vermont Diabetes Information System on inpatient and emergency room use: results from a randomized trial" 2678: 2318: 2089: 631: 309: 2183: 1910: 676: 610: 2705: 2330: 2223: 2134: 1947: 1935: 1844: 1533: 2400: 2325: 1766: 1334:"The Vermont diabetes information system: a cluster randomized trial of a population based decision support system" 343:
A similar system, The New York City A1C Registry, is in used to monitor the estimated 600,000 diabetic patients in
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in 2008. More flu-related searches are taken to indicate higher flu activity. The results, which were published in
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aggregate data from multiple systems, across geo-political boundaries and include an automated alerting process.
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management and care improvement than for epidemiological monitoring of an entire population or catchment area.
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Search aggregates have been most frequently used to track and model influenza. A popular example is
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is conducted on these registries to determine governmental funding for research and prevention.
434: 115:, as well as social problems such as domestic violence, are increasingly being integrated into 2380: 2375: 2303: 2252: 2026: 2006: 1994: 1954: 1925: 1893: 1811: 1704: 1672: 1568: 1483: 1412: 1363: 1314: 1283: 1183: 1122: 1068: 1050: 1011: 907: 842: 787: 717: 658: 625: 597: 580: 531: 513: 438: 302: 265: 252: 211: 203: 996: 206:, who began work on such a system in 2004. Inspired by these early, encouraging experiences, 2545: 2298: 2257: 2069: 2044: 1856: 1786: 1630: 1473: 1465: 1402: 1394: 1353: 1345: 1273: 1265: 1254:"Diabetes decision support: initial experience with the Vermont diabetes information system" 1173: 1163: 1112: 1102: 1058: 1042: 1001: 991: 951: 897: 889: 834: 777: 707: 570: 562: 521: 505: 426: 408: 215: 679:. United States Centers for Disease Control and Prevention. 13 January 2006. Archived from 2606: 2308: 2111: 2104: 2039: 1979: 1861: 1851: 1781: 1754: 1739: 1694: 1684: 1635: 376: 188: 101: 646: 885: 830: 773: 756:
Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (February 2009).
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Littenberg B, MacLean CD, Zygarowski K, Drapola BH, Duncan JA, Frank CR (March 2009).
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Syndromic surveillance is the analysis of medical data to detect or anticipate
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Techniques of public health surveillance have been used in particular to study
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A syndromic surveillance system based on search queries was first proposed by
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Brownstein, John S.; Freifeld, Clark C.; Madoff, Lawrence C. (2009-05-21).
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and public health professionals are alerted that there may be an issue.
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complications of diabetes, a formal evaluation has not yet been done.
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Kilbridge PM, Campbell UC, Cozart HB, Mojarrad MG (Jul–Aug 2006).
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World Health Organization: Immunization, Vaccines and Biologicals
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Maclean CD, Gagnon M, Callas P, Littenberg B (December 2009).
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Collection, analysis and interpretation of health-related data
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is increasingly being explored by public health officials."
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Committee on the Environment, Public Health and Food Safety
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Sharpe JD, Hopkins RS, Cook RL, Striley CW (October 2016).
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institutions that oversee healthcare providers (such as
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Journal of the American Medical Informatics Association
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Some conditions, especially chronic diseases such as
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2472: 2469: 2468: 2466: 2462: 2459: 2457: 2454: 2453: 2451: 2447: 2444: 2443: 2441: 2437: 2434: 2433: 2431: 2430: 2428: 2426:Organizations 2424: 2421: 2413: 2407: 2404: 2402: 2399: 2397: 2394: 2392: 2389: 2387: 2384: 2382: 2379: 2377: 2374: 2372: 2369: 2367: 2364: 2362: 2359: 2358: 2356: 2350: 2342: 2339: 2337: 2334: 2333: 2332: 2329: 2327: 2324: 2320: 2317: 2315: 2312: 2310: 2307: 2305: 2302: 2300: 2297: 2295: 2292: 2290: 2287: 2286: 2284: 2283: 2281: 2275: 2269: 2266: 2264: 2263:Vaccine trial 2261: 2259: 2256: 2254: 2251: 2249: 2246: 2244: 2241: 2237: 2234: 2233: 2232: 2229: 2225: 2222: 2221: 2220: 2217: 2213: 2210: 2209: 2208: 2205: 2203: 2200: 2199: 2197: 2191: 2185: 2182: 2178: 2176: 2172: 2170: 2168: 2163: 2161: 2158: 2156: 2153: 2151: 2148: 2147: 2146: 2143: 2141: 2140:Relative risk 2138: 2136: 2133: 2131: 2128: 2127: 2125: 2119: 2113: 2110: 2106: 2103: 2101: 2100:Health equity 2098: 2096: 2093: 2092: 2091: 2088: 2086: 2083: 2081: 2078: 2076: 2073: 2071: 2068: 2066: 2065:Health system 2063: 2061: 2058: 2056: 2055:Global health 2053: 2051: 2048: 2046: 2043: 2041: 2038: 2036: 2035:Biostatistics 2033: 2032: 2030: 2028: 2024: 2018: 2015: 2013: 2010: 2008: 2005: 2003: 2000: 1996: 1993: 1991: 1988: 1986: 1983: 1981: 1978: 1976: 1973: 1971: 1968: 1967: 1966: 1963: 1961: 1958: 1956: 1953: 1949: 1946: 1945: 1944: 1941: 1937: 1934: 1932: 1929: 1927: 1924: 1922: 1919: 1917: 1914: 1912: 1909: 1908: 1907: 1904: 1900: 1897: 1895: 1892: 1890: 1887: 1885: 1882: 1881: 1880: 1877: 1873: 1870: 1868: 1865: 1864: 1863: 1860: 1858: 1855: 1853: 1850: 1846: 1843: 1842: 1841: 1838: 1837: 1835: 1833: 1829: 1823: 1820: 1818: 1815: 1813: 1810: 1808: 1805: 1803: 1800: 1798: 1795: 1793: 1790: 1788: 1785: 1783: 1780: 1778: 1777:Right to food 1775: 1773: 1770: 1768: 1765: 1763: 1760: 1756: 1753: 1751: 1748: 1746: 1743: 1741: 1738: 1736: 1733: 1732: 1731: 1728: 1726: 1723: 1721: 1718: 1715: 1711: 1710:Mental health 1708: 1706: 1703: 1701: 1698: 1696: 1693: 1691: 1688: 1686: 1683: 1679: 1676: 1674: 1671: 1670: 1669: 1666: 1664: 1661: 1659: 1658:Housing First 1656: 1652: 1649: 1647: 1646:Health system 1644: 1643: 1642: 1641:Health policy 1639: 1637: 1634: 1632: 1629: 1627: 1624: 1622: 1619: 1617: 1614: 1612: 1609: 1605: 1602: 1600: 1597: 1596: 1595: 1592: 1590: 1587: 1585: 1582: 1580: 1577: 1575: 1572: 1570: 1567: 1565: 1562: 1561: 1559: 1555: 1551: 1550:Public health 1544: 1539: 1537: 1532: 1530: 1525: 1524: 1521: 1503: 1497: 1494: 1489: 1485: 1480: 1475: 1471: 1467: 1463: 1459: 1455: 1448: 1445: 1440: 1436: 1432: 1426: 1423: 1418: 1414: 1409: 1404: 1400: 1396: 1392: 1388: 1384: 1377: 1374: 1369: 1365: 1360: 1355: 1351: 1347: 1343: 1339: 1335: 1328: 1325: 1320: 1316: 1313:(3): 166–70. 1312: 1308: 1304: 1297: 1294: 1289: 1285: 1280: 1275: 1271: 1267: 1263: 1259: 1255: 1248: 1245: 1234:on 2011-02-02 1233: 1229: 1223: 1220: 1208: 1204: 1197: 1194: 1189: 1185: 1180: 1175: 1170: 1165: 1160: 1155: 1151: 1147: 1143: 1136: 1133: 1128: 1124: 1119: 1114: 1109: 1104: 1100: 1096: 1092: 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Retrieved 471:the original 466: 428: 420: 365: 361:Bexar County 354: 342: 307: 300: 287:dengue fever 282:Influenzanet 279: 262: 259:Social media 250: 241: 201: 196:bioterrorist 193: 185: 179:or go to an 169: 161:bioterrorist 158: 154:bioterrorism 144: 125: 106: 99: 91:bioterrorism 60: 56: 42: 38: 34: 30: 29: 2679:WikiProject 2419:and history 2299:Engineering 2012:Vaccination 1884:Food safety 1212:2 September 1101:(2): e161. 990:: 101–118. 322:cholesterol 314:blood sugar 89:, and even 2695:Categories 2432:Caribbean 2309:Processing 2243:Quarantine 2165:Student's 1965:Sanitation 1599:History of 1508:2014-04-18 1238:2014-04-18 1159:2003.08086 1152:(17): 17. 932:2014-04-18 742:2014-04-18 617:2014-04-18 477:19 October 393:References 326:creatinine 293:, Brazil. 167:or a lab. 139:See also: 2612:John Snow 2539:Education 2529:Full list 2417:education 2341:ISO 22000 2294:Chemistry 2207:Epidemics 2160:ROC curve 1970:Emergency 1750:Radiation 1730:Pollution 1714:Ministers 1611:Euthenics 1207:The Verge 1055:0028-4793 855:206553739 706:: 244–8. 518:0008-543X 316:control ( 210:launched 172:influenza 165:physician 150:outbreaks 79:influenza 2655:Category 2354:sciences 2289:Additive 1960:Safe sex 1931:Medicine 1845:Theories 1616:Genomics 1594:Eugenics 1584:Deviance 1564:Auxology 1488:18633070 1417:20975923 1368:19862578 1319:19298097 1288:16507723 1188:33558607 1127:27765731 1073:19423867 1016:31905322 912:26234783 847:24626916 792:19020500 722:17238340 663:15714620 657:: 5–11. 626:cite web 598:disa.mil 585:16622159 536:29205307 371:See also 113:diabetes 69:and the 2667:Commons 2580:History 2477:Canada 2452:Europe 1936:Nursing 1916:Hygiene 1879:Hygiene 1604:Liberal 1557:General 1479:2509589 1408:2958673 1359:2787948 1279:1470550 1179:7870878 1118:5095368 1064:2917042 1007:7959655 903:4522652 882:Bibcode 827:Bibcode 819:Science 770:Bibcode 713:1839505 576:1513675 527:5846186 232:Twitter 147:disease 2467:India 2442:China 2314:Safety 1995:Worker 1486:  1476:  1415:  1405:  1366:  1356:  1317:  1286:  1276:  1186:  1176:  1125:  1115:  1071:  1061:  1053:  1014:  1004:  910:  900:  853:  845:  800:125775 798:  790:  762:Nature 720:  710:  661:  583:  573:  534:  524:  516:  498:Cancer 441:  216:Nature 208:Google 109:stroke 33:(also 2492:U.S. 2336:HACCP 2285:Food 2177:-test 2169:-test 1755:Light 1740:Water 1154:arXiv 851:S2CID 796:S2CID 291:Bahia 129:JCAHO 2268:WASH 2224:List 2212:List 1745:Soil 1484:PMID 1413:PMID 1364:PMID 1315:PMID 1284:PMID 1214:2020 1184:PMID 1123:PMID 1069:PMID 1051:ISSN 1012:PMID 908:PMID 843:PMID 788:PMID 718:PMID 704:2006 659:PMID 632:link 581:PMID 532:PMID 514:ISSN 479:2016 439:ISBN 328:and 226:and 83:SARS 1735:Air 1474:PMC 1466:doi 1403:PMC 1395:doi 1354:PMC 1346:doi 1274:PMC 1266:doi 1174:PMC 1164:doi 1113:PMC 1103:doi 1059:PMC 1043:doi 1039:360 1002:PMC 992:doi 952:doi 898:PMC 890:doi 835:doi 823:343 778:doi 766:457 708:PMC 600:PDF 571:PMC 563:doi 522:PMC 506:doi 502:123 435:548 320:), 310:NIH 289:in 87:HIV 67:WHO 41:or 2697:: 1482:. 1472:. 1462:98 1460:. 1456:. 1433:. 1411:. 1401:. 1389:. 1385:. 1362:. 1352:. 1342:24 1340:. 1336:. 1311:15 1309:. 1305:. 1282:. 1272:. 1262:96 1260:. 1256:. 1205:. 1182:. 1172:. 1162:. 1148:. 1144:. 1121:. 1111:. 1097:. 1093:. 1081:^ 1067:. 1057:. 1049:. 1037:. 1033:. 1010:. 1000:. 988:41 986:. 982:. 966:^ 906:. 896:. 888:. 876:. 872:. 849:. 841:. 833:. 821:. 817:. 794:. 786:. 776:. 764:. 760:. 716:. 702:. 698:. 655:53 653:. 649:. 628:}} 624:{{ 579:. 569:. 559:13 557:. 553:. 530:. 520:. 512:. 500:. 496:. 465:. 453:^ 437:. 411:, 401:^ 340:. 272:. 183:. 85:, 81:, 37:, 2531:) 2527:( 2175:Z 2167:t 1716:) 1712:( 1542:e 1535:t 1528:v 1511:. 1490:. 1468:: 1419:. 1397:: 1391:1 1370:. 1348:: 1321:. 1290:. 1268:: 1241:. 1216:. 1190:. 1166:: 1156:: 1150:4 1129:. 1105:: 1099:2 1075:. 1045:: 1018:. 994:: 960:. 954:: 935:. 914:. 892:: 884:: 878:5 857:. 837:: 829:: 802:. 780:: 772:: 745:. 724:. 665:. 634:) 620:. 587:. 565:: 538:. 508:: 481:. 447:. 20:)

Index

Syndromic Surveillance
World Health Organization
public health
infectious diseases
WHO
Centers for Disease Control and Prevention
public health informatics
influenza
SARS
HIV
bioterrorism
2001 anthrax attacks
cancer registry
stroke
diabetes
epidemiologic
cost-benefit analysis
JCAHO
Disease surveillance
disease
outbreaks
bioterrorism
bioterrorist
physician
influenza
emergency telephone number
emergency department
epidemiologists
bioterrorist
Gunther Eysenbach

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