Dataset Information

Optimizing provider recruitment for influenza surveillance networks.

ABSTRACT: The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods.

SUBMITTER: Scarpino SV

PROVIDER: S-EPMC3325176 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Optimizing provider recruitment for influenza surveillance networks.

Scarpino Samuel V SV Dimitrov Nedialko B NB Meyers Lauren Ancel LA

PLoS computational biology 20120412 4

The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When appli ...[more]

PMID: 22511860

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Project description:With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.

Project description:BackgroundThis report describes the results of recruitment efforts and the subsequent participation of pregnant women in study activities in a 2010-2012 observational study focused on influenza illness and vaccination in California and Oregon, USA.MethodsSocio-demographic and health characteristics extracted from electronic medical records were compared among pregnant women who enrolled in the study, refused to participate, or were never reached for study invitation. These characteristics plus additional self-reported information were compared between women who enrolled in two study tracks: a prospective cohort vs. women enrolled following an acute respiratory illness (ARI) medical encounter. The characteristics of women who participated in weekly ARI surveillance (cohort enrollees, year one) and a 6-month follow-up interview (all enrollees) were also examined.ResultsIn year one, we reached 51% (6938/13,655) of the potential participants we tried to contact by telephone, and 20% (1374/6938) of the women we invited agreed to join the prospective cohort. Women with chronic medical conditions, pregnancy complications, and medical encounters for ARI (prior to pregnancy or during the study period) were more likely to be reached for recruitment and more likely to enroll in the cohort. Twenty percent of cohort enrollees never started weekly surveillance reports; among those who did, reports were completed for 55% of the surveillance weeks. Receipt of the influenza vaccine was higher among women who joined the cohort (76%) than those who refused (56%) or were never reached (54%). In contrast, vaccine uptake among medical enrollees in year one (54%; 53/98) and two (52%; 79/151) was similar to other pregnant women in those years. Study site, white race, non-Hispanic ethnicity, and not having a child aged < 13 years at home were most consistently associated with joining as a cohort or medical enrollee and completing study activities after joining.ConclusionsWe observed systematic differences in socio-demographic and health characteristics across different levels of participant engagement and between cohort and medical enrollees. More methodological research and innovation in conducting prospective observational studies in this population are needed, especially when extended participant engagement and ongoing surveillance are required.

Dataset Information

Optimizing provider recruitment for influenza surveillance networks.

Publications

Optimizing provider recruitment for influenza surveillance networks.

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