Tracking health seeking behavior during an Ebola outbreak via mobile phones and SMS

npj Digital Medicine

Volumn 1, Issue 1, 2018, Pages

  Feng, Shuo ^a   Grépin, Karen A ^b   Chunara, Rumi ^c,d  

^a UNIVERSITY OF WATERLOO   (Canada)

^b WILFRID LAURIER UNIVERSITY   (Canada)

^c NEW YORK UNIVERSITY   (United States)

^d POLYTECHNIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISEASES; POPULATION STATISTICS; SURVEYS;

EMERGENCY SITUATION; HEALTH SURVEYS; HEALTHCARE UTILIZATION; HOUSEHOLD SURVEYS; INDIVIDUAL LEVELS; MEASUREMENTS OF; POPULATION LEVELS; PROPENSITY SCORE MATCHING; SIMILAR PATTERN; WEST AFRICA;

CELLULAR TELEPHONES;

ADULT; AGED; ARTICLE; EBOLA HEMORRHAGIC FEVER; EPIDEMIC; GOVERNMENT; HEALTH CARE; HEALTH CARE DELIVERY; HEALTH CARE UTILIZATION; HELP SEEKING BEHAVIOR; HUMAN; INFORMATION PROCESSING; MAJOR CLINICAL STUDY; PRIORITY JOURNAL; PROPENSITY SCORE;

EID: 85089606058     PISSN: None     EISSN: 23986352     Source Type: Journal    
DOI: 10.1038/s41746-018-0055-z     Document Type: Article

References (33)

1. Shannon, F. et al. Effects of the 2014 Ebola outbreak on antenatal care and delivery outcomes in Liberia: a nationwide analysis. Public Health Action 7, S88–S93 (2017)
2. Farrar, J. J. & Piot, P. The Ebola emergency—immediate action, ongoing strategy. New Engl. J. Med. 371, 1545–1546 (2014)
3. Morse, B., Grépin, K. A., Blair, R. A. & Tsai, L. Patterns of demand for non-Ebola health services during and after the Ebola outbreak: panel survey evidence from Monrovia, Liberia. BMJ Glob. Health 1, e000007 (2016)
4. Centers for Disease Control and Prevention. Recommendations for managing and preventing cases of malaria in areas with Ebola (Ebola Virus Disease) https://www.cdc.gov/vhf/ebola/index.html (2015)
5. Zachariah, R. et al. Ebola, fragile health systems and tuberculosis care: a call for pre-emptive action and operational research. Int. J. Tuberc. Lung Dis. 19, 1271–1275 (2015)
6. Dynes, M. M. et al. Perceptions of the risk for Ebola and health facility use among health workers and pregnant and lactating women—Kenema District, Sierra Leone. MMWR Morb. Mortal. Wkly. Rep. 63, 1226–1227 (2015)
7. Delamou, A., Hammonds, R. M., Caluwaerts, S., Utz, B. & Delvaux, T. Ebola in Africa: beyond epidemics, reproductive health in crisis. Lancet 384, 2105 (2014)
8. Iyengar, P., Kerber, K., Howe, C. J. & Dahn, B. Services for mothers and newborns during the ebola outbreak in Liberia: the need for improvement in emergencies. PLoS Curr. 7 (2015)
9. Ly, J. et al. Facility-based delivery during the Ebola virus disease epidemic in rural Liberia: analysis from a cross-sectional, population-based household survey. PLoS Med. 13, e1002096 (2016)
10. Wagenaar, B. H. et al. The2014–2015 Ebola virus disease outbreak and primary healthcare delivery in Liberia: time-series analyses for 2010–2016. PLoS Med. 15, e1002508 (2018)
11. Gizelis, T.-I., Karim, S., Østby, G. & Urdal, H. Maternal health care in the time of Ebola: a mixed-method exploration of the impact of the epidemic on delivery services in Monrovia. World Dev. 98, 169–178 (2017)
12. Ribacke, K. J. B. et al. The impact of the West Africa Ebola outbreak on obstetric health care in Sierra Leone. PLoS One 11, e0150080 (2016)
13. Chunara, R., Smolinski, M. S. & Brownstein, J. S. Why we need crowdsourced data in infectious disease surveillance. Curr. Infect. Dis. Rep. 15, 316–319 (2013)
14. Steinhubl, S. R., Kim, K.-i, Ajayi, T. & Topol, E. J. Virtual care for improved global health. Lancet 391, 419 (2018)
15. Rehman, N., Liu, J., & Chunara, R. "Using propensity score matching to understand the relationship between online health information sources and vaccination sentiment." In Proceedings of Association for the Advancement of Artificial Intelligence Spring Symposia, pp. 23-25. 2016
16. Chunara, R., Wisk, L. E. & Weitzman, E. R. Denominator issues for personally generated data in population health monitoring. Am. J. Prev. Med. 52, 549–553 (2017)
17. Leo, B., Morello, R., Mellon, J., Peixoto, T., Davenport, S. Do mobile phone surveys work in poor countries? Working Paper 398. Center for Global Development; Washington, DC, 2015
18. Battaglia, M. P., Hoaglin, D. C. & Frankel, M. R. Practical considerations in raking survey data. Surv. Pract. 2, 1-10 (2013)
19. Pew Global. Cell Phones in Africa: Communication Lifeline. Available at: http://www.pewglobal.org/2015/04/15/cell-phones-in-africa-communication-lifeline/ (2015). [Accessed 01 May 2018]
20. Rosenbaum, P. R. & Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983)
21. Hirano, K. & Imbens, G. W. Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Serv. Outcomes Res. Methodol. 2, 259–278 (2001)
22. United Nations Statistics Division. Development of National Statistical Systems. http://unstats.un.org/unsd/dnss/default.aspx. Accessed 17 Nov 2015
23. Liberia Institute of Statistics and Geo-Information Services. Population and Housing Census, 2008 Population and Housing Census https://www.lisgis.net/pg_img/NPHC%202008%20Final%20Report.pdf (2009). Accessed Feb 28, 2018
24. Lerer, A., Ward, M., & Amarasinghe, S. (2010, December). Evaluation of IVR data collection UIs for zuntrained rural users. In Proceedings of the first ACM symposium on computing for development (p. 2)
25. Ho, D. E., Imai, K., King, G. & Stuart, E. A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Anal. 15, 199–236 (2007)
26. Lonestar Cell. https://en.wikipedia.org/wiki/Lonestar_Cell. Accessed Sept. 10, 2018
27. Poushter, J. & Oates, R. Cell Phones in Africa: Communication Lifeline. (Pew Research Centre, Washington DC, 2015)
28. Corsi, D. J., Neuman, M., Finlay, J. E. & Subramanian, S. Demographic and health surveys: a profile. Int. J. Epidemiol. 41, 1602–1613 (2012)
29. Stanton, C., Abderrahim, N. & Hill, K. DHS Maternal Mortality Indicators: An Assessment of Data Quality And Implications for Data Use (Macro International Calverton, 1997)
30. Liberia Institute of Statistics and Geo-Information Services (LISGIS), Ministry of Health and Social Welfare [Liberia], National AIDS Control Program [Liberia], and ICF International. 2014. Liberia Demographic and Health Survey 2013. Monrovia, Liberia: Liberia Institute of Statistics and Geo-Information Services (LISGIS) and ICF International
31. Austin, P. C. & Mamdani, M. M. A comparison of propensity score methods: a case‐study estimating the effectiveness of post‐AMI statin use. Stat. Med. 25, 2084–2106 (2006)
32. Lanza, S. T., Moore, J. E. & Butera, N. M. Drawing causal inferences using propensity scores: a practical guide for community psychologists. Am. J. Community Psychol. 52, 380–392 (2013)
33. Cohen, J. Statistical power analysis for the behavioral sciences (2nd edn) (Erlbaum Associates, Hillsdale 1988)