Asthma is one of the most common chronic diseases that has a profound impact on people’s well-being and our society. In this study, we link multiple large-scale data sources to construct an epidemiological model to predict asthma prevalence across geographic regions. We use: (1) the Social Media Monitoring (SMM) data from Twitter (N = 500 million tweets/day), (2) the 2014 Behavioral Risk Factor Surveillance System (BRFSS) (N = 464,664), and (3) the 2014 American Community Survey (ACS) conducted by the U.S. Census Bureau (N = 3.5 million per year). We predict asthma prevalence in the traditional survey (BRFSS) using social media information collected from Twitter and socioeconomic factors collected from ACS. The evidence suggests that monitoring asthma-related tweets may provide real-time information that can be used to predict outcomes from traditional surveys.
AkinbamiLaura J.LaFleurBonnie J.SchoendorfKenneth C.2002. Racial and income disparities in childhood asthma in the United States. Ambulatory Pediatrics2 (5): 382–87.
2.
AligneAndrew C.AuingerPeggyByrdRobert S.WeitzmanMichael. 2000. Risk factors for pediatric asthma. Contributions of poverty, race, and urban residence. American Journal of Respiratory and Critical Care Medicine162 (3Pt 1): 873–77.
3.
AyersJohn W.AlthouseBenjamin M.AllemJon-PatrickLeasEric C.DredzeMarkWilliamsRebecca S.2016. Revisiting the rise of electronic nicotine delivery systems using search query surveillance. American Journal of Preventive Medicine50 (6): e173–e181.
4.
AyersJohn W.RibislKurt M.BrownsteinJohn S.2011. Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. American Journal of Preventive Medicine40 (4): 448–53.
5.
BenevenutoFabrícioMagnoGabrielRodriguesTiagoAlmeidaVirgílio. 2010. Detecting spammers on Twitter. (CEAS 2010) Seventh Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference. Available from http://www.decom.ufop.br/fabricio/download/ceas10.pdf.
6.
BroniatowskiDavid A.PaulMichael J.DredzeMark. 2013. National and local influenza surveillance through Twitter: An analysis of the 2012–2013 influenza epidemic. PLoS One8 (12): e83672.
Centers for Disease Control and Prevention (CDC). 2015b. Behavioral risk factor surveillance system. Available from http://www.cdc.gov/brfss/.
9.
DaiHongyingHaoJianqiang. 2016. Mining social media data for opinion polarities about electronic cigarettes. Tobacco Control. doi:10.1136/tobaccocontrol-2015-052818
10.
DaniulaityteRamintaNahhasRamzi W.WijeratneSanjavaCarlsonRobert G.LamyFrancois R.MartinsSilvia S.BoyerEdward W.SmithG. AlanShethAmit. 2015. “Time for dabs”: Analyzing Twitter data on marijuana concentrates across the U.S. Drug and Alcohol Dependence155:307–11.
GillinghamGwendolynConwayMichael A.ChapmanWendy W.CasaleMichael B.PettigrewKathryn B.2013. #wheezing: A content analysis of asthma-related tweets. Online Journal of Public Health Informatics5 (1). doi:10.5210/ojphi.v5i1.4591.
GoldDiane R.WrightRosalind. 2005. Population disparities in asthma. Annual Review of Public Health26:89–113.
15.
GowersAlison M.CullinanPaulAyresJon G.AndersonH. RossStrachanDavid P.HolgateStephen T.MillsInga C.MaynardRobert L.2012. Does outdoor air pollution induce new cases of asthma? Biological plausibility and evidence; a review. Respirology17 (6): 887–98.
HoskinsG.McCowanC.NevilleR. G.ThomasG. E.SmithB.SilvermanS.2000. Risk factors and costs associated with an asthma attack. Thorax55 (1): 19–24.
18.
HsuJoyQinXiaotingBeaversSuzanne F.MirabelliMaria C.2016. Asthma-related school absenteeism, morbidity, and modifiable factors. American Journal of Preventive Medicine51 (1): 23–32.
19.
HuangJidongKornfieldRachelSzczypkaGlenEmerySherry. 2014. A cross-sectional examination of marketing of electronic cigarettes on Twitter. Tobacco Control23 (Suppl 3): iii26–iii30.
20.
LiuBin. 2012. Sentiment analysis and opinion mining. San Rafael, CA: Morgan & Claypool Publishers.
21.
McLeishAlison C.ZvolenskyMichael J.2010. Asthma and cigarette smoking: A review of the empirical literature. Journal of Asthma47 (4): 345–61.
22.
MurthyDhiraj. 2013. Twitter: Social communication in the Twitter age. Cambridge: Polity.
23.
NutiSudhakar V.WaydaBrianRanasingheIsuruWangSisiDreyerRachel P.ChenSerene I.MurugiahKarthik. 2014. The use of Google trends in health care research: A systematic review. PLoS One9 (10): e109583.
24.
OddyWendy H.SherriffJill L.de KlerkNicholas H.KendallGarth E.SlyPeter D.BeilinLawrence J.BlakeKevin B.LandauLouis I.StanleyFiona J.2004. The relation of breastfeeding and body mass index to asthma and atopy in children: A prospective cohort study to age 6 years. American Journal of Public Health94 (9): 1531–37.
25.
OpolskiMelissaWilsonIan. 2005. Asthma and depression: A pragmatic review of the literature and recommendations for future research. Clinical Practice & Epidemiology in Mental Health. doi:10.1186/1745-0179-1-18
26.
PelatCamilleTurbelinClementBar-HenAvnerFlahaultAntoineValleronAlain-Jacques. 2009. More diseases tracked by using Google Trends. Letter to the editor. Emerging Infectious Disease15 (8): 1327–28.
27.
Platts-MillsThomas A. E.CarterMelody C.1997. Asthma and indoor exposure to allergens. New England Journal of Medicine336 (19): 1382–84.
28.
RamSudhaZhangWenliWilliamsMaxPengetnzeYolande. 2015. Predicting asthma-related emergency department visits using big data. IEEE Journal of Biomedical Health Informatics19 (4): 1216–23.
29.
SalathéMarcelKhandelwalShashank. 2011. Assessing vaccination sentiments with online social media: Implications for infectious disease dynamics and control. PLoS Computational Biology7 (10): e1002199.
30.
SchwartzJeolSlaterDanielLarsonTimothy V.PiersonWilliam E.KoenigJane Q.1993. Particulate air pollution and hospital emergency room visits for asthma in Seattle. American Review of Respiratory Disease147 (4): 826–31.
31.
ShayaFadia T.ManevalMark S.GbarayorConfidence M.SohnKyongseiDalalAnand A.DuDongyiScharfSteven M.2009. Burden of COPD, asthma, and concomitant COPD and asthma among adults: Racial disparities in a Medicaid population. Chest136 (2): 405–11.
TetreaultLouis-FrancoisDoucetMarieveGamachePhilippeFournierMichelBrandAllanKosatskyTomSmargiassiAudrey. 2016. Childhood exposure to ambient air pollutants and the onset of asthma: An administrative cohort study in Quebec. Environmental Health Perspectives124 (8): 1276–82.
34.
TolbertPaige E.MulhollandJames A.MacIntoshDavid L.XuFanDanielsDanniDevineOwen J.CarlinBradley P.KleinMitchelButlerAndre J.NordenbergDale F.FrumkinHowardRyanP. BarryWhiteMacy C.2000. Air quality and pediatric emergency room visits for asthma in Atlanta, Georgia, USA. American Journal of Epidemiology151 (8): 798–810.
von MutiusE.SchwartzJ.NeasL. M.DockeryD.WeissS. T.2001. Relation of body mass index to asthma and atopy in children: The National Health and Nutrition Examination Study III. Thorax56 (11): 835–38.
37.
VosenSimeonSchmidtTorsten. 2011. Forecasting private consumption: survey-based indicators vs. Google trends. Journal of Forecasting30 (6): 565–78.
38.
WhittemoreA. S.KornE. L.1980. Asthma and air pollution in the Los Angeles area. American Journal of Public Health70 (7): 687–96.
39.
WilliamsDavid R.SternthalMichelleWrightRosalind J.2009. Social determinants: Taking the social context of asthma seriously. Pediatrics123 (Suppl 3): S174–S184.
40.
WongCharlene A.SapMaartenSchwartzAndrewTownRobertBakerTomUngarLyleMerchantRaina M.2015. Twitter sentiment predicts Affordable Care Act marketplace enrollment. Journal of Medical Internet Research17 (2):e51.
41.
YoungSean D.RiversCaitlinLewisBryan. 2014. Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Preventive Medicine63:112–15.
