2014.ISDS.Abstracts.Final.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
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ISDS 2014 Conference Abstracts

Using Big Healthcare Data to Supplement Chikungunya 
Surveillance in the U.S.
Joel Greenspan*1 and Silvia Valkova2

1Martin, Blanck & Associates, Atlanta, GA, USA; 2IMS Health Government Solutions, Fairfax, VA, USA

Objective
This paper describes how high-volume electronic healthcare (HC) 

reimbursement claims (eHRCs) from providers’ offices can be used 
to supplement Chikungunya surveillance in the U.S.

Introduction
Chikungunya virus disease (CHIK) is a mosquito-borne viral 

infection currently widespread in the Caribbean with the potential 
for emergence and endemicity in the U.S. via infected travelers and 
local mosquito vectors. CHIK disease can be severe and disabling 
with symptoms similar to dengue. CHIK is not a U.S. nationally 
notifiable disease and tracking travel-associated and locally acquired 
cases is currently dependent on voluntary reporting via ArboNET. 
While ArboNET cases are laboratory confirmed and highly specific, 
ArboNET is a passive surveillance system where representativeness 
and timeliness may be lacking. In contrast, submitting an electronic 
bill following HC services is the most mature and widely used 
form of eHealth. Providers are highly motivated to submit claims 
for reimbursement and the eHRC process is ubiquitous in the U.S. 
HC system. HIPAA-compliant eHRCs from provider offices can 
be captured in e-commerce and consolidated into electronic data 
warehouses and used for many purposes including public health 
surveillance. eHRCs are standardized and each claim contains pertinent 
person, place, and time information as well as ICD-9 diagnostic codes. 
IMS Health (IMS) is a global HC information company and maintains 
one of world’s largest eHealth data warehouses that processes ~1 
billion provider office eHRCs annually. IMS consolidates eHRCs 
from >60% of all U.S. office-based providers from all parts of the 
U.S. The size and predictability of the eHRC flow into the IMS data 
warehouse supports projections of national estimates and time trends 
of conditions of interest.

Methods
For this study we compared CHIK cases reported via ArboNET 

from January to August 2014 with eHRC provider claims with an 
ICD-9 code of 066.3 (Other Mosquito-borne Fever) that had been 
received in the IMS data warehouse during the same time period. We 
classified 066.3 patients as suspect CHIK cases. eHRC data elements 
examined included unique patient ID, month of provider visit, and 
patient State and ZIP-3. For patients with multiple provider 066.3 
visits, the earliest visit was used for analysis.

Results
During the observation period ArboNET reported 584 CHIK cases 

from 42 States and D.C. IMS noted 509 suspect cases from 40 states. 
The distribution of States with any CHIK cases was similar between 
ArboNet and IMS. However, ArboNET reported cases in 5 States 
where IMS detected none; IMS detected suspect cases in 2 states 
where ArboNET reported none. ArboNET reported many more cases 
in 2 States (FL, TN); IMS reported many more suspect cases in 5 
States (IL, MI, NY, PA, WV). The State with the most ArboNET 
cases was FL (142); the State with the most IMS suspect cases was 
NY (125). IMS ZIP-3 analysis demonstrated a high proportion of 
suspect cases in several high density urban areas (NYC metro and 

Northern NJ, Philadelphia, Detroit). IMS suspect cases in FL were 
noted in Miami and other major population areas.

Conclusions
eHRCs may help overcome some of the limitations of ArboNET in 

documenting timing, occurrence, and spread of CHIK in the U.S., but 
not without additional research to verify the predictive value of ICD-9 
066.3 and whether providers are using this code based on known 
laboratory values or general awareness of the disease, its symptoms, 
and patient recent travel history.

Keywords
Chikungunya; ArboNet; healthcare claims; big data

*Joel Greenspan
E-mail: greenspan@comcast.net    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e78, 201