BackgroundTwitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter’s relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.ObjectiveThe intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.MethodsFrom the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords “flu”, “influenza”, “gripe”, and “high fever”. The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.ResultsInfection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay’s Center and the Atlantic Avenue Terminal.ConclusionsWhile others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter’s strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.

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Objective To qualitatively assess the barriers and facilitators to uptake of referral services amongst high-risk pregnant women in rural Rajasthan. Methods A purposive sample of pregnant women with high-risk conditions requiring referral follow-up care (severe hypertension, moderate anemia, and severe anemia) were considered for inclusion. In-depth individual interviews were conducted in the local dialect, Mewari. Interviews were transcribed, coded, and organized for thematic generation as per the analytical framework described in the socio-ecological model. Results 19 high risk pregnant women of low socioeconomic backgrounds across 15 villages were interviewed. Barriers to referral care included lack of transportation, household responsibilities, and limited awareness, education, and social support. The most prominent barrier was lack of accompaniment to the referral center by a family member or health worker. Facilitators included available husbands, engaged heath workers, supportive neighbors, and other female family members who shared past experiences. Conclusions Social support at the interpersonal and community level was key to overcoming referral care barriers faced by high-risk pregnant women in rural Rajasthan. Interventions that enhance social support may improve uptake of referral care services by high-risk pregnant women.