Deploying digital health data to optimize influenza surveillance at national and local scales.

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.

The decline of malaria in Vietnam, 1991-2014.

BACKGROUND: Despite the well-documented clinical efficacy of artemisinin-based combination therapy (ACT) against malaria, the population-level effects of ACT have not been studied thoroughly until recently. An ideal case study for these population-level effects can be found in Vietnam's gradual adoption of artemisinin in the 1990s. METHODS AND RESULTS: Analysis of Vietnam's national annual malaria reports (1991-2014) revealed that a 10% increase in artemisinin procurement corresponded to a 32.8% (95% CI 27.7-37.5%) decline in estimated malaria cases. There was no consistent national or regional effect of vector control on malaria. The association between urbanization and malaria was generally negative and sometimes statistically significant. CONCLUSIONS: The decline of malaria in Vietnam can largely be attributed to the adoption of artemisinin-based case management. Recent analyses from Africa showed that insecticide-treated nets had the greatest effect on lowering malaria prevalence, suggesting that the success of interventions is region-specific. Continuing malaria elimination efforts should focus on both vector control and increased access to ACT.

Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference.

Spatial big data have the velocity, volume, and variety of big data sources and contain additional geographic information. Digital data sources, such as medical claims, mobile phone call data records, and geographically tagged tweets, have entered infectious diseases epidemiology as novel sources of data to complement traditional infectious disease surveillance. In this work, we provide examples of how spatial big data have been used thus far in epidemiological analyses and describe opportunities for these sources to improve disease-mitigation strategies and public health coordination. In addition, we consider the technical, practical, and ethical challenges with the use of spatial big data in infectious disease surveillance and inference. Finally, we discuss the implications of the rising use of spatial big data in epidemiology to health risk communication, and public health policy recommendations and coordination across scales.

Spatial distribution and determinants of childhood vaccination refusal in the United States.

Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012-2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measurement of vaccine refusal. Our work highlights fine-scale socio-demographic characteristics associated with vaccine refusal nationally, finds that spatial clustering in refusal can be explained by such factors, and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake.

Clinical Outcomes in Critically Ill Coronavirus Disease 2019 Patients: A Unique New York City Public Hospital Experience.

To explore demographics, comorbidities, transfers, and mortality in critically ill patients with confirmed severe acute respiratory syndrome coronavirus 2. DESIGN: Retrospective cohort study. SETTING: Data were collected from a large tertiary care public hospital ICU that is part of the largest public healthcare network in the United States. PATIENTS: One-hundred thirty-seven adult (≥ 18 yr old) ICU patients admitted between March 10, 2020, and April 7, 2020, with follow-up collected through May 18, 2020. INTERVENTIONS: None. MEASUREMENTS: Demographic, clinical, laboratory, treatment, and outcome data extracted from electronic medical records. MAIN RESULTS: The majority of patients were male (99/137; 72.3%) and older than 50 years old (108/137; 78.9%). The most reported ethnicity and race were Hispanic (61/137; 44.5%) and Black (23/137; 16.7%). One-hundred six of 137 patients had at least one comorbidity (77.4%). One-hundred twenty-one of 137 (78.1%) required mechanical ventilation of whom 30 (24.8%) moved to tracheostomy and 46 of 137 (33.6%) required new onset renal replacement therapy. Eighty-two of 137 patients (59.9%) died after a median of 8 days (interquartile range 5-15 d) in the ICU. Male sex had a trend toward a higher hazard of death (hazard ratio, 2.1 [1.1-4.0]) in the multivariable Cox model. CONCLUSIONS: We report a mortality rate of 59.9% in a predominantly Hispanic and Black patient population. A significant association between comorbidities and mortality was not found in multivariable regression, and further research is needed to study factors that impact mortality in critical coronavirus disease 2019 patients. We also describe how a public hospital developed innovative approaches to safely manage a large volume of interhospital transfers and admitted patients.