scholarly journals Power, Potential, and Pitfalls of Surveillance using Clinical Ancillary Services Data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Beth T. Poitras ◽  
Rebecca S. Payne ◽  
Nicholas D. Seliga

ObjectiveDiscuss the power of utilizing DOD clinical ancillary services data for infectious disease surveillance, the steps used to mitigate pitfalls which may occur during the surveillance process, and the potential of adapting this data for surveillance of emerging infectious diseases.IntroductionMilitary service members and their families work and live around the world where both endemic and emerging infectious diseases are common. Timely infectious disease surveillance helps to inform medical and policy decisions which ensure mission readiness and beneficiary health. The EpiData Center (EDC) at the Navy and Marine Corps Public Health Center has performed public health surveillance, including routine infectious disease monitoring among service members, their families, and others eligible for military medical benefits for the Department of the Navy (DON) and Department of Defense (DOD) since 2005.The EDC stores and maintains 15 databases totaling over 20 terabytes of health and administrative data. These include administrative data from outpatient encounters and inpatient admissions, Health Level-7 (HL7) formatted ancillary services data, and medical event reports. These data provide the potential for robust surveillance methodologies to monitor diseases of interest and identify trends and outbreaks. The primary intent and design of these data sources is not for disease surveillance, but rather for administrative and billing purposes. However, due to the availability of this data, it is routinely used by academic organizations, private industry, health systems, and government organizations to conduct health surveillance and research. Ancillary services data in particular can be very powerful for near-real time infectious disease surveillance in the DOD as the aggregated data is available within 1 to 2 days after processing. The EDC has demonstrated the value of using laboratory data for surveillance through outbreak detection and longitudinal health trends for specific diseases among select populations.The fact that this data is not designed for surveillance does present several pitfalls in regards to analysis, from issues ranging from free text interpretation to changing testing practices. These pitfalls can be mitigated through standardized processes and detailed quality assurance testing. The EDC has harnessed the power of available administrative health data to improve health outcomes and influence policy among military beneficiaries.MethodsThe EDC has established and validated methods for using and interpreting ancillary services data. Key steps involved in the process for infectious disease surveillance include:● Reviewing diagnostic criteria;● Defining relevant search terms and test types;-● Consulting clinicians for technical input when needed;● Developing algorithms using retrospective data;● Developing quality checks;● Automating the process to reduce daily workload;● Documenting processes and methods.● Variables essential to interpretation within ancillary services records are not standardized across the DOD.Several pitfalls can occur during the surveillance process due to complexities related to free text, layout of the full results, and differences between laboratory practices. Typically, these pitfalls can be grouped into one of the following categories:● Data irregularities that include unexpected abbreviations and numerous misspellings; this may result in misclassification or missed cases.● Data changes resulting from shifts in testing practices due to new or discontinued laboratory tests, or differing data entry methods.● Classification challenges for diseases that require sequential testing or clinical compatibility information, which limits the ability to positively identify cases. However, records can be identified as ‘suspect cases’ (i.e., syphilis, Lyme disease, varicella, yellow fever and others).● Technical issues, at the medical facility, server, or EDC level, often causes lapses in data, which results in a delay in case reporting.Despite these pitfalls, their impact can be mitigated by routinely reviewing algorithms, employing data analytic techniques that account for likely misspellings and abbreviations, and incorporating data quality checks that flag unexpected or unclassifiable results. Outside of automated processes, human interaction is important; EDC analysts must remain astute and vigilant to investigate unusual or unexpected occurrences, shifts in the volume of cases or data.ResultsDue to the pitfalls outlined, the EDC has developed powerful and robust methods to circumvent the issues of using administrative health data for near real-time clinical ancillary services based disease surveillance. The methods developed to address the pitfalls of working with administrative health data have been used in the daily active surveillance of over fifty reportable infectious diseases, weekly surveillance of influenza, and monthly surveillance of malaria and tuberculosis. In addition to using these methods for routine surveillance, the EDC adapts this methodology for new reports for specific concerns. Further, the EDC continues to develop and adapt these methodologies to quickly address emerging infectious threats and the pitfalls associated with the data. Pharmacy transactions and administrative data from outpatient encounters and inpatient discharges supplement and enhance laboratory-based surveillance, particularly when only a diagnosis or presumptive treatment occurs (such as with influenza). While this method provides timely information, built in quality assurance checks and routine reviews of algorithms must occur to address changes in testing practices, the use of new tests, variation in laboratory technician entry of results, and to ensure data integrity.ConclusionsThe EDCs comprehensive surveillance provides the DON and DOD leadership and preventive medicine community with the ability to monitor and respond to ongoing and emerging infectious disease threats. While the primary purpose of administrative health data is not for health surveillance, the EDC has recognized the rich source of health information which may be extracted from this data. Processes have been developed to mitigate the pitfalls that may occur when administrative data is adapted for health surveillance. This data provides a real-time snapshot of the health of military beneficiaries and provides awareness of possible outbreaks, health trends, and geographic hotspots. Beyond routine surveillance this data has the potential to be used to rapidly create new methodologies to detect emerging infections which can be combined with other data sources, such as pharmacy transactions and medical encounters, to provide a more robust picture of cases by accounting for variance in clinical practice. This data often guides military health policy and procedures and is essential for a medically ready force. 

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Kassim ◽  
C Arinze ◽  
D Tom-Aba ◽  
O Adeoye ◽  
C Ihekweazu ◽  
...  

Abstract Introduction The PANDORA-ID-NET consortium aims to build capacity for effective outbreak response in sub-Saharan Africa. Part of this mission is to develop a real-time data sharing platform for disease outbreaks that leverages centralised data management and uses mobile technologies for data gathering and feedback. We have committed to using open-source technologies, so that the platform can be deployed on regional IT infrastructure and further developed by local staff, and collected data can be stored and processed in the region of origin. This abstract aims to describe how we identified a state of the art open-source system that fulfils these criteria, and the process of how we are extending it to function within the current infectious disease control framework in Tanzania, under our partnership with the Ifakara Health Institute (IHI). Methods To find state of the art open-source systems matching our criteria, we performed a rapid review of the literature. We screened 1022 articles and found 15 candidate systems, out of which only SORMAS satisfied the criteria. SORMAS was developed jointly by the Helmholtz Centre for Infection Research (HZI) and the Nigeria CDC, and was modeled on Nigeria's successful response to the Ebola outbreak. The system can be used for case management, contact tracing, surveillance, and laboratory sample management. Data is collected and synchronised using Android mobile devices (both online and offline) and data aggregation and analysis are performed in real-time via a web application Results Having chosen SORMAS, we established a collaboration between the SORMAS developer team and the PANDORA team. IHI are guiding ongoing work on adapting SORMAS to the Tanzanian health facility geography and the country's case definition guidelines for notifiable diseases. Conclusions Once adapted for Tanzania, SORMAS will fill an unoccupied niche in infectious disease control, improving the quality of collected case data and enabling better outbreak response Key messages A state of the art, mobile-based, open-source outbreak management and infectious disease surveillance system (SORMAS) is being deployed in Tanzania. We outline our experience with piloting SORMAS in Tanzania, building on the experience of our Nigerian and German partners, who rolled out this system nationally in Nigeria and other African countries.


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Mariana G Casal ◽  
R M Bravo-Clouzet ◽  
I Ruberto ◽  
T Jue ◽  
R Guerrero ◽  
...  

Objective: To describe 5 years of binational infectious disease surveillance using the binational variable in the medical electronic surveillance system in ArizonaIntroduction: Infectious diseases can spread across borders. (1) The Arizona Department of Health Services (ADHS) collects information on binational infectious disease cases and shares it with Mexico. Infectious disease investigation in Arizona is enhanced by using an electronic surveillance platform known as the Medical Electronic Disease Surveillance Intelligence System (MEDSIS), and in 2010 a specific variable for binational cases with Mexico was added to the platform. ADHS also maintains a binational case definition in the state reportable communicable morbidities manual. Arizona partners with the US Centers for Disease Control and Prevention (CDC)’s Division of Global Migration and Quarantine (DGMQ), US Mexico Unit (USMU), in a monthly binational case reporting project, and shares information with the Ministry of Health of the State of Sonora, Mexico, (SON MOH) to reinforce ongoing communication, to establish baseline disease patterns, and to help detect binational outbreaks. In 2007, the Ministry of Health of the State of Sonora began to use the MEDSIS system for real-time secure case notification, and secure file sharing, using the Arizona’s Health Services Portal and secure e-mail accounts for confidential communication between both states.Methods: From 2011 to 2015, the ADHS Binational Border Infectious Disease Surveillance (BIDS) program maintained a database to collect information on binational cases with Mexico, and coordinated regularly with SON MOH to investigate and respond to binational cases and outbreaks. In addition, a SAS program was created to search for possible binational cases not designated as binational using variables such as an address in Mexico and Mexican citizenship. The ADHS BIDS program investigated all suspected binational cases with Mexico and classified them as binational according to the case definition established by the Council of State and Territorial Epidemiologists (CSTE). The ADHS BIDS program also shared binational cases from Mexico with CDC DGMQ USMU through an Epi-X Forum and with SON MOH through MEDSIS or secure e-mail.Results: Between 2011 and 2015, the ADHS BIDS program investigated 2,158 possible binational cases with Mexico. From those, 70.44% (n=1520) were classified as binational with Mexico according with the CSTE case definition. The majority of cases were classified as binational because of travel to Mexico (n =1089, 71.6%), with 59% traveling to Sonora (n =641). The majority of cases during those 5 years were enteric diseases (n=1086, 71.4%), followed by vector borne diseases (n =131, 8.6%). Most of the binational cases reported had symptom onset between June and August, following the seasonality of both southbound travel and enteric diseases. Regular communication with Sonora facilitated detection of an average of three binational outbreaks per year. All confirmed binational cases were reported to CDC DGMQ USMU and SON MOH.Conclusions: Continuous sharing of infectious disease surveillance information between both states is essential in understanding the magnitude and types of reportable diseases in the Arizona/Sonora border region. Proper use of the binational MEDSIS variable enables a quicker identification of binational status, allowing for the prompt investigation of possible binational cases and detection of binational outbreaks. Binational outbreaks led to collaborative Arizona/Sonora investigations on several occasions, strengthening our relationship, coordination, collaborations, and understanding of the surveillance system used by SON MOH. Real-time exchange of information using the same secure surveillance system enables better situational awareness, more timely and accurate binational communication, and binational collaborations.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bettina Habib ◽  
Robyn Tamblyn ◽  
Nadyne Girard ◽  
Tewodros Eguale ◽  
Allen Huang

Abstract Background Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection. Methods We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons. Results Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician. Conclusion Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.


Sign in / Sign up

Export Citation Format

Share Document