A novel surveillance approach for disaster mental health

PLoS ONE

Volumn 12, Issue 7, 2017, Pages

  Gruebner, Oliver ^a   Lowe, Sarah R ^b   Sykora, Martin ^c   Shankardass, Ketan ^d   Subramanian, S V ^a   Galea, Sandro ^e  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b MONTCLAIR STATE UNIVERSITY   (United States)

^c LOUGHBOROUGH UNIVERSITY   (United Kingdom)

^d WILFRID LAURIER UNIVERSITY   (Canada)

^e BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUTE STRESS; ANGER; ARTICLE; COMMUNITY MENTAL HEALTH; CONTROLLED STUDY; DISASTER; DISGUST; FEAR; GEOGRAPHIC INFORMATION SYSTEM; HUMAN; NEW YORK; SADNESS; SOCIAL MEDIA; EMOTION; HEALTH SURVEY; HURRICANE; MENTAL DISORDERS; MENTAL HEALTH; PROCEDURES;

CYCLONIC STORMS; DISASTERS; EMOTIONS; GEOGRAPHIC INFORMATION SYSTEMS; HUMANS; MENTAL DISORDERS; MENTAL HEALTH; NEW YORK; POPULATION SURVEILLANCE; SOCIAL MEDIA;

EID: 85027701949     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0181233     Document Type: Article

References (59)

1. International Federation of Red Cross and Red Crescent Societies. World disasters report: Focus on culture and risk. In: httpwww.ifrc.org [Internet]. 2014 [cited 20 Dec 2015]. Available: http://www.ifrc.org/Global/Documents/Secretariat/201410/WDR%202014.pdf
2. Hoeppe P. Trends in weather related disasters–Consequences for insurers and society. Weather and Climate Extremes. 2015. https://doi.org/10.1016/j.wace.2015.10.002
3. Norris FH, Friedman MJ, Watson PJ, Byrne CM, Diaz E, Kaniasty K. 60,000 disaster victims speak: Part I. An empirical review of the empirical literature, 1981–2001. Psychiatry. 2002; 65: 207–239. PMID: 12405079
4. Goldmann E, Galea S. Mental Health Consequences of Disasters. Annu Rev Public Health. Annual Reviews; 2014; 35: 169–183. https://doi.org/10.1146/annurev-publhealth-032013-182435 PMID: 24159920
5. Neria Y, Shultz JM. Mental health effects of Hurricane Sandy: characteristics, potential aftermath, and response. JAMA. 2012; 308: 2571–2572. https://doi.org/10.1001/jama.2012.110700 PMID: 23160987
6. Fussell E, Lowe SR. The impact of housing displacement on the mental health of low-income parents after Hurricane Katrina. Social Science & Medicine. 2014; 113: 137–144. https://doi.org/10.1016/j.socscimed.2014.05.025 PMID: 24866205
7. Freedy JR, Shaw DL, Jarrell MP, Masters CR. Towards an understanding of the psychological impact of natural disasters: An application of the conservation resources stress model. Journal of Traumatic Stress. Wiley Subscription Services, Inc., A Wiley Company; 1992; 5: 441–454. https://doi.org/10.1002/jts.2490050308
8. Kaniasty K, Norris FH. A test of the social support deterioration model in the context of natural disaster. J Pers Soc Psychol. American Psychological Association; 1993; 64: 395–408. https://doi.org/10.1037/0022-3514.64.3.395 PMID: 8468668
9. Hobfoll SE. Conservation of resources. A new attempt at conceptualizing stress. Am Psychol. 1989; 44: 513–524. PMID: 2648906
10. Galea S, Brewin CR, Gruber M, Jones RT, King DW, King LA, et al. Exposure to hurricane-related stressors and mental illness after Hurricane Katrina. Arch Gen Psychiatry. American Medical Association; 2007; 64: 1427–1434. https://doi.org/10.1001/archpsyc.64.12.1427 PMID: 18056551
11. Kessler RC, Galea S, Gruber MJ, Sampson NA, Ursano RJ, Wessely S. Trends in mental illness and suicidality after Hurricane Katrina. Mol Psychiatry. 2008; 13: 374–384. https://doi.org/10.1038/sj.mp. 4002119 PMID: 18180768
12. Ekman P. Basic Emotions. Handbook of Cognition and Emotion. Chichester, UK: John Wiley & Sons, Ltd; 2005. pp. 45–60. https://doi.org/10.1002/0470013494.ch3
13. Lowe SR, Galea S. The Mental Health Consequences of Mass Shootings. Trauma Violence Abuse. SAGE Publications; 2015; 1524838015591572. https://doi.org/10.1177/1524838015591572
14. Galea S, Ahern J, Resnick H, Kilpatrick D, Bucuvalas M, Gold J, et al. Psychological sequelae of the September 11 terrorist attacks in New York City. N Engl J Med. 2002; 346: 982–987. https://doi.org/10.1056/NEJMsa013404 PMID: 11919308
15. Harvey AG, Bryant RA. The relationship between acute stress disorder and posttraumatic stress disorder: a 2-year prospective evaluation. J Consult Clin Psychol. 1999; 67: 985–988. PMID: 10596520
16. Paul MJ, Dredze M. A model for mining public health topics from Twitter. Health. 2012.
17. Paul MJ, Dredze M. Discovering Health Topics in Social Media Using Topic Models. Lambiotte R, editor. PLoS ONE. Public Library of Science; 2014; 9: e103408. https://doi.org/10.1371/journal.pone. 0103408 PMID: 25084530
18. Althouse BM, Allem J-P, Childers MA, Dredze M, Ayers JW. Population health concerns during the United States’ Great Recession. Am J Prev Med. Elsevier; 2014; 46: 166–170. https://doi.org/10.1016/j.amepre.2013.10.008 PMID: 24439350
19. Yang J-A, Tsou M-H, Jung C-T, Allen C, Spitzberg BH, Gawron JM, et al. Social media analytics and research testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages. Big Data & Society. SAGE Publications; 2016; 3: 2053951716652914.
20. De Choudhury M. Can social media help us reason about mental health? New York, New York, USA: ACM; 2014. pp. 1243–1244. 10.1145/2567948.2580064
21. Ayers JW, Althouse BM, Dredze M. Could Behavioral Medicine Lead the Web Data Revolution? JAMA. American Medical Association; 2014; 311: 1399–1400. https://doi.org/10.1001/jama.2014.1505 PMID: 24577162
22. Coppersmith G, Dredze M, Harman C. From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. 2015.
23. Hirschberg J, Manning CD. Advances in natural language processing. Science. American Association for the Advancement of Science; 2015; 349: 261–266. https://doi.org/10.1126/science.aaa8685 PMID: 26185244
24. Sykora MD, Jackson TW, O’Brien A. Emotive ontology: Extracting fine-grained emotions from terse, informal messages. IADIS International Journal on Computer Science and Information Systems. 2013.
25. Sykora MD, Jackson TW, O’Brien A. National Security and Social Media Monitoring: A Presentation of the EMOTIVE and Related Systems. 2013 European Intelligence and Security Informatics Conference. IEEE; 2013;: 172–175. 10.1109/EISIC.2013.38
26. Glass K, Colbaugh R. Estimating the sentiment of social media content for security informatics applications. Secur Inform. Springer-Verlag; 2012; 1: 1–16. https://doi.org/10.1186/2190-8532-1-3
27. Bollen J, Mao H, Pepe A. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. ICWSM. 2011.
28. Ayers JW, Althouse BM, Allem J-P, Childers MA, Zafar W, Latkin C, et al. Novel surveillance of psychological distress during the great recession. J Affect Disord. Elsevier; 2012; 142: 323–330. https://doi.org/10.1016/j.jad.2012.05.005 PMID: 22835843
29. De Choudhury M, Counts S, Horvitz E. Social media as a measurement tool of depression in populations. New York, New York, USA: ACM Press; 2013. pp. 47–56. 10.1145/2464464.2464480
30. Thelwall M, Buckley K, Paltoglou G. Sentiment in Twitter events. Journal of the Association for Information Science and Technology. Wiley Subscription Services, Inc., A Wiley Company; 2011; 62: 406–418. https://doi.org/10.1002/asi.21462
31. De Choudhury M, Monroy-Hernández A, Mark G. “Narco” emotions. New York, New York, USA: ACM Press; 2014. pp. 3563–3572. 10.1145/2556288.2557197
32. Kang K, Yoon C, Kim EY. Identifying depressive users in Twitter using multimodal analysis. International Conference on Big Data and Smart Computing (BigComp). IEEE; 2016;: 231–238. 10.1109/ BIGCOMP.2016.7425918
33. Mowery D, Smith HA, Cheney T, Bryan C, Conway M. Identifying Depression-Related Tweets from Twitter for Public Health Monitoring. Online Journal of Public Health Informatics. 2016; 8.
34. Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems. 2015; 89: 14–46. https://doi.org/10.1016/j.knosys.2015.06.015
35. Coppersmith G, Dredze M, Harman C. Quantifying Mental Health Signals in Twitter. Stroudsburg, PA, USA: Association for Computational Linguistics; 2014. pp. 51–60. https://doi.org/10.3115/v1/w14-3207
36. Coppersmith G, Harman C, Dredze M. Measuring Post Traumatic Stress Disorder in Twitter. ICWSM. 2014.
37. Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. Ferguson N, editor. PLoS Comp Biol. 2013; 9: e1003256.
38. Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science. 2014; 343: 1203–1205. https://doi.org/10.1126/science.1248506 PMID: 24626916
39. Sutton J, Gibson CB, Phillips NE, Spiro ES, League C, Johnson B, et al. A cross-hazard analysis of terse message retransmission on Twitter. Proc Natl Acad Sci USA. National Acad Sciences; 2015; 112: 14793–14798. https://doi.org/10.1073/pnas.1508916112 PMID: 26627233
40. Houston JB, Hawthorne J, Perreault MF, Park EH, Goldstein Hode M, Halliwell MR, et al. Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters. 2015; 39: 1–22. https://doi.org/10.1111/disa.12092 PMID: 25243593
41. Johansson F, Brynielsson J, Quijano MN. Estimating Citizen Alertness in Crises Using Social Media Monitoring and Analysis. IEEE; 2012. pp. 189–196. 10.1109/EISIC.2012.23
42. Kumar S, Barbier G, Abbasi MA, Liu H. TweetTracker: An Analysis Tool for Humanitarian and Disaster Relief. ICWSM. 2011.
43. Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes Twitter users: real-time event detection by social sensors. WWW’10. New York, New York, USA: ACM; 2010;: 851–860. 10.1145/1772690.1772777
44. Kryvasheyeu Y, Chen H, Obradovich N, Moro E, Van Hentenryck P, Fowler J, et al. Rapid assessment of disaster damage using social media activity. Science Advances. American Association for the Advancement of Science; 2016; 2: e1500779–e1500779. https://doi.org/10.1126/sciadv.1500779 PMID: 27034978
45. Nagar R, Yuan Q, Freifeld CC, Santillana M, Nojima A, Chunara R, et al. A Case Study of the New York City 2012–2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives. Journal of Medical Internet Research. 2014; 16: e236. https://doi.org/10.2196/jmir. 3416 PMID: 25331122
46. Kleinman K, Abrams AM, Kulldorff M, Platt R. A model-adjusted space-time scan statistic with an application to syndromic surveillance. Epidemiology and Infection. Cambridge University Press; 2005; 133: 409–419. PMID: 15962547
47. Lawson AB, Kleinman K. Spatial and Syndromic Surveillance for Public Health. John Wiley & Sons; 2005.
48. Kulldorff M, Heffernan R, Hartman J, Assunção R, Mostashari F. A Space–Time Permutation Scan Statistic for Disease Outbreak Detection. PLoS Med. 2005; 2: e59. https://doi.org/10.1371/journal.pmed. 0020059 PMID: 15719066
49. Gruebner O, Sykora M, Lowe SR, Shankardass K, Trinquart L, Jackson T, et al. Mental health surveillance after the terrorist attacks in Paris. The Lancet. Elsevier; 2016; 387: 2195–2196. https://doi.org/10.1016/S0140-6736(16)30602-X
50. Lui M, Baldwin T. langid.py: an off-the-shelf language identification tool. Proceedings of the ACL 2012 system demonstrations. Association for Computational Linguistics; 2012;: 25–30.
51. Shuyo N. Language Detection Library for Java [Internet]. GitHub; 2011. Available: https://github.com/shuyo/language-detection
52. Hoffmann W, Latza U, Terschüren C. Leitlinien und Empfehlungen zur Sicherung von Guter Epidemiologischer Praxis (GEP)—überarbeitete Fassung nach Evaluation. Das Gesundheitswesen. Georg Thieme Verlag KG Stuttgart New York; 2005; 67: 217–225. https://doi.org/10.1055/s-2004-813850
53. Ekman PE, Davidson RJE. The nature of emotion: Fundamental questions. Oxford University Press; 1994.
54. Kulldorff M, Information Management Services Inc. SaTScan v9.4.1: Software for the spatial and space-time scan statistics [Internet]. 2015. Available: http://www.satscan.org/
55. Jones SG, Kulldorff M. Influence of Spatial Resolution on Space-Time Disease Cluster Detection. Bejon P, editor. PLoS ONE. Public Library of Science; 2012; 7: e48036. https://doi.org/10.1371/journal.pone.0048036 PMID: 23110167
56. Neumeister L. Breezy Point Fire: At Least 80 Flooded Houses Destroyed By NYC Blaze. The Huffington Post. 2012.
57. Sloan L, Morgan J. Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter. PLoS ONE. Public Library of Science; 2015; 10: e0142209. https://doi.org/10.1371/journal.pone.0142209 PMID: 26544601
58. Kulldorff M, Mostashari F, Duczmal L, Yih WK, Kleinman K, Platt R. Multivariate scan statistics for disease surveillance. Statist Med. 2007; 26: 1824–1833. https://doi.org/10.1002/sim.2818 PMID: 17216592
59. FEMA Modeling Task Force. FEMA MOTF Hurricane Sandy Impact Analysis. In: fema.maps.arcgis. com [Internet]. 2015 [cited 10 May 2017]. Available: http://fema.maps.arcgis.com/home/item.html?id= 307dd522499d4a44a33d7296a5da5ea0.