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 socioenvironmental 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

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

<div><p>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.</p></div

Temporal and spatial group effects for the epidemic intensity and epidemic duration surveillance models.

<p>A) The posterior mean and 95% credible intervals for group (random) effects are shown for log epidemic intensity. B) Continental U.S. county map for fitted log epidemic intensity for an example flu season (2006-2007). C) The posterior mean and 95% credible intervals for group (random) effects are shown for log epidemic duration. D) Continental U.S. county map for fitted epidemic duration for an example flu season (2006-2007).</p

Discrepancies between state and county surveillance models for epidemic intensity.

<p>A) Comparison of state and county surveillance models (left and right columns, respectively) for log epidemic intensity for states with overestimation and underestimation with the state surveillance model —Montana (top row) and South Carolina (bottom row), respectively. B) Aggregation bias between county and state epidemic intensity surveillance models for the 2006-2007 influenza season, where error is defined as the difference between fitted values for county and state log epidemic intensity. Negative error (blue) indicates that the state-level surveillance model underestimated risk relative to the county-level surveillance model, and vice versa.</p

Final model predictors, hypotheses, and data availability.

<p>Final model predictors, hypotheses, and data availability.</p

Optimizing design of sentinel surveillance systems.

<p>A) Diagram indicating changes to model inference as fewer fixed-location sentinels reported data. Color indicates directionality of the significant effect (blue is positive, red is negative) while greater transparency indicates a lower percentage of replicates with a significant effect (for models with missingness); dot size represents the magnitude of the posterior mean (or average of the posterior mean across replicates). Predictors with no significant effect across the sequence of models were removed for viewing ease, and absence of a dot means the effect was not significant across any replicates. B) Map of model prediction match between the complete model and the 80, 40, 20, and 5% reporting levels for fixed-location sentinels. Match between the complete and sentinel models were aggregated across 70 season-replicate combinations (7 seasons * 10 replicates). Color indicates match between posterior predictions in the missing and complete models (purple represents a failure to match in at least 10% of season-replicate combinations). Failure to match means that the interquartile ranges for two posterior distributions failed to overlap with each other C) Scatterplot of county match percentage between the complete and sentinel models versus the total volume of medical claims visits. Each point represents a single season-replicate combination, and colors represent the reporting level of the fixed-location sentinels. The dashed line indicates the average visit volume in CDC’s ILINet during the study period, and it corresponds roughly with the 5% reporting level for our medical claims database.</p

Socio-environmental and measurement factors associated with the epidemic intensity surveillance model.

<p>For the total population multi-season epidemic intensity models, these are the means and 95% credible intervals for the posterior distributions of the A) socio-environmental coefficients and B) measurement-related coefficients. Distributions indicated in green were statistically significant (95% credible interval deviated from zero). Coefficients are reported according to their effect on log epidemic intensity.</p

The decline of malaria in Vietnam, 1991–2014

Abstract 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