Data Blindspots: High-Tech Disease Surveillance Misses the Poor

Scarpino, Samuel V.; Scott, James G.; Eggo, Rosalind M; Dimitrov, Nedialko B.; Meyers, Lauren Ancel

doi:10.5210/ojphi.v8i1.6451

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…We found that locations with greater poverty had lower influenza disease burden, in contrast to previous evidence for heightened rates of influenza-related hospitalizations, influenza-like illness, respiratory illness, neglected chronic diseases, and other measures of poor health among populations with greater material deprivation [ 43 , 44 , 47 , 58 – 63 ]. Differences in socio-economic background may change recognition and therefore reporting of disease symptoms [ 46 , 58 ]. Material deprivation and lack of social cohesion have also been implicated in lower rates of health care utilization for ILI, which would reduce the observation of influenza disease burden in our medical claims data among the poorest populations [ 44 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, it is important to consider the possibility that individual patient behavior may bias the reporting of ILI disease burden, thus driving observed spatial heterogeneity. The association between poverty and social determinants [ 41 – 46 ], access to care, care-seeking behavior, and health insurance coverage [ 47 – 49 ], and reported ILI disease burden has been treated extensively elsewhere.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Younger and older persons who are at risk of more severe reactions to foodborne diseases might be underrepresented. Furthermore, studies suggest that representativeness in Internet-based systems and data sources for disease surveillance are influenced by factors such as gender, education, and income [ 24 - 26 ].…”

Section: Discussionmentioning

confidence: 99%

A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports

Quade¹,

Nsoesie

2017

JMIR Public Health Surveill

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BackgroundUnderreporting of foodborne illness makes foodborne disease burden estimation, timely outbreak detection, and evaluation of policies toward improving food safety challenging.ObjectiveThe objective of this study was to present and evaluate Iwaspoisoned.com, an openly accessible Internet-based crowdsourcing platform that was launched in 2009 for the surveillance of foodborne illness. The goal of this system is to collect data that can be used to augment traditional approaches to foodborne disease surveillance.MethodsIndividuals affected by a foodborne illness can use this system to report their symptoms and the suspected location (eg, restaurant, hotel, hospital) of infection. We present descriptive statistics of users and businesses and highlight three instances where reports of foodborne illness were submitted before the outbreaks were officially confirmed by the local departments of health.ResultsMore than 49,000 reports of suspected foodborne illness have been submitted on Iwaspoisoned.com since its inception by individuals from 89 countries and every state in the United States. Approximately 95.51% (42,139/44,119) of complaints implicated restaurants as the source of illness. Furthermore, an estimated 67.55% (3118/4616) of users who responded to a demographic survey were between the ages of 18 and 34, and 60.14% (2776/4616) of the respondents were female. The platform is also currently used by health departments in 90% (45/50) of states in the US to supplement existing programs on foodborne illness reporting.ConclusionsCrowdsourced disease surveillance through systems such as Iwaspoisoned.com uses the influence and familiarity of social media to create an infrastructure for easy reporting and surveillance of suspected foodborne illness events. If combined with traditional surveillance approaches, these systems have the potential to lessen the problem of foodborne illness underreporting and aid in early detection and monitoring of foodborne disease outbreaks.

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“…These disparities in reporting are important for identifying bias and quantifying population representation; factors that could impact the robustness of non-traditional disease surveillance systems ( Althouse et al, 2015 ). While in some cases aggregation at higher geographical levels can capture overall trends in illness, systems that rely on event reports might be missing vulnerable and poor populations ( Scarpino et al, 2016 ; Nsoesie et al, 2016 ). Further research is needed to evaluate the potential association between digital reports of illness and socio-economic status, drawing on individual-level data.…”

Section: Discussionmentioning

confidence: 99%

Disparities in digital reporting of illness: A demographic and socioeconomic assessment

Henly

Tuli

Kluberg

et al. 2017

Preventive Medicine

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Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors. The results show that reviews of foodservice businesses and digital reports of foodborne illness follow a clear seasonal pattern with higher reporting observed in the summer, when most foodborne outbreaks are reported and to a lesser extent in the winter months. Additionally, factors typically associated with affluence (such as, higher median income and fraction of the population with a bachelor's degrees) were positively correlated with foodborne illness reports. However, restaurants per capita and education were the most significant predictors of illness reporting at the US county level. These results suggest that well-known health disparities might also be reflected in the online environment. Although this is an observational study, it is an important step in understanding disparities in the online public health environment.

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Data Blindspots: High-Tech Disease Surveillance Misses the Poor

Cited by 6 publications

References 6 publications

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

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

A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports

Disparities in digital reporting of illness: A demographic and socioeconomic assessment

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