An Assessment of Information Exchange Practices, Challenges, and Opportunities to Support US Disease Surveillance in 3 States.

BACKGROUND: State and local public health agencies collect and use surveillance data to identify outbreaks, track cases, investigate causes, and implement measures to protect the public's health through various surveillance systems and data exchange practices. PURPOSE: The purpose of this assessment was to better understand current practices at state and local public health agencies for collecting, managing, processing, reporting, and exchanging notifiable disease surveillance information. METHODS: Over an 18-month period (January 2014-June 2015), we evaluated the process of data exchange between surveillance systems, reporting burdens, and challenges within 3 states (California, Idaho, and Massachusetts) that were using 3 different reporting systems. RESULTS: All 3 states use a combination of paper-based and electronic information systems for managing and exchanging data on reportable conditions within the state. The flow of data from local jurisdictions to the state health departments varies considerably. When state and local information systems are not interoperable, manual duplicative data entry and other work-arounds are often required. The results of the assessment show the complexity of disease reporting at the state and local levels and the multiple systems, processes, and resources engaged in preparing, processing, and transmitting data that limit interoperability and decrease efficiency. CONCLUSIONS: Through this structured assessment, the Centers for Disease Control and Prevention (CDC) has a better understanding of the complexities for surveillance of using commercial off-the-shelf data systems (California and Massachusetts), and CDC-developed National Electronic Disease Surveillance System Base System. More efficient data exchange and use of data will help facilitate interoperability between National Notifiable Diseases Surveillance Systems.

Underascertainment of acute hepatitis C virus infections in the U.S. surveillance system: a case series and chart review.

BACKGROUND: In 2010, the incidence of hepatitis C virus (HCV) infection in the United States was estimated to be 17 000 cases annually, based on 850 acute HCV cases reported to the Centers for Disease Control and Prevention by local public health authorities. Absence of symptomatic disease and lack of a specific laboratory test for acute infection complicates diagnosis and surveillance. OBJECTIVE: To validate estimates of the incidence of acute HCV infection by determining the reporting rate of clinical diagnoses of acute infection to the Massachusetts Department of Public Health (MDPH) and Centers for Disease Control and Prevention. DESIGN: Case series and chart review. SETTING: Two hospitals and the state correctional health care system in Massachusetts. PATIENTS: 183 patients clinically diagnosed with acute HCV infection from 2001 to 2011 and participating in a research study. MEASUREMENTS: Rate of electronic case reporting of acute HCV infection to the MDPH and rate of subsequent confirmation according to national case definitions. RESULTS: 149 of 183 (81.4%) clinical cases of acute HCV infection were reported to the MDPH for surveillance classification. The MDPH investigated 43 of these reports as potential acute cases of HCV infection based on their surveillance requirements; ultimately, only 1 met the national case definition and was counted in nationwide statistics published by the Centers for Disease Control and Prevention. Discordance in clinical and surveillance classification was often related to missing clinical or laboratory data at the MDPH as well as restrictive definitions, including requirements for negative hepatitis A and B laboratory results. LIMITATION: Findings may not apply to other jurisdictions because of differences in resources for surveillance. CONCLUSION: Clinical diagnoses of acute HCV infection were grossly underascertained by formal surveillance reporting. Incomplete clinician reporting, problematic case definitions, limitations of diagnostic testing, and imperfect data capture remain major limitations to accurate case ascertainment despite automated electronic laboratory reporting. These findings may have implications for national estimates of the incidence of HCV infection. PRIMARY FUNDING SOURCE: National Institutes of Health.

Integrating clinical practice and public health surveillance using electronic medical record systems.

Electronic medical record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. We describe a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.

Covid-19 Rates by Time since Vaccination during Delta Variant Predominance.

BACKGROUND: With the emergence of the delta variant, the United States experienced a rapid increase in Covid-19 cases in 2021. We estimated the risk of breakthrough infection and death by month of vaccination as a proxy for waning immunity during a period of delta variant predominance. METHODS: Covid-19 case and death data from 15 U.S. jurisdictions during January 3 to September 4, 2021 were used to estimate weekly hazard rates among fully vaccinated persons, stratified by age group and vaccine product. Case and death rates during August 1 to September 4, 2021 were presented across four cohorts defined by month of vaccination. Poisson models were used to estimate adjusted rate ratios comparing the earlier cohorts to July rates. RESULTS: During August 1 to September 4, 2021, case rates per 100,000 person-weeks among all vaccine recipients for the January to February, March to April, May to June, and July cohorts were 168.8 (95% confidence interval [CI], 167.5 to 170.1), 123.5 (95% CI, 122.8 to 124.1), 83.6 (95% CI, 82.9 to 84.3), and 63.1 (95% CI, 61.6 to 64.6), respectively. Similar trends were observed by age group for BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) vaccine recipients. Rates for the Ad26.COV2.S (Janssen-Johnson & Johnson) vaccine were higher; however, trends were inconsistent. BNT162b2 vaccine recipients 65 years of age or older had higher death rates among those vaccinated earlier in the year. Protection against death was sustained for the mRNA-1273 vaccine recipients. Across age groups and vaccine types, people who were vaccinated 6 months ago or longer (January-February) were 3.44 (3.36 to 3.53) times more likely to be infected and 1.70 (1.29 to 2.23) times more likely to die from COVID-19 than people vaccinated recently in July 2021. CONCLUSIONS: Our study suggests that protection from SARS-CoV-2 infection among all ages or death among older adults waned with increasing time since vaccination during a period of delta predominance. These results add to the evidence base that supports U.S. booster recommendations, especially for older adults vaccinated with BNT162b2 and recipients of the Ad26.COV2.S vaccine. (Funded by the Centers for Disease Control and Prevention.).

Enhancing surveillance for hepatitis C through public health informatics.

Disease surveillance for hepatitis C in the United States is limited by the occult nature of many of these infections, the large volume of cases, and limited public health resources. Through a series of discrete processes, the Massachusetts Department of Public Health modified its surveillance system in an attempt to improve timeliness and completeness of reporting and case follow-up of hepatitis C. These processes included clinician-based reporting, electronic laboratory reporting, deployment of a Web-based disease surveillance system, automated triage of pertinent data, and automated character recognition software for case-report processing. These changes have resulted in an increase in the timeliness of reporting.

Geographic Associations Between Social Factors and SARS-CoV-2 Testing Early in the COVID-19 Pandemic, February-June 2020, Massachusetts.

OBJECTIVES: Widespread SARS-CoV-2 testing is critical to identify infected people and implement public health action to interrupt transmission. With SARS-CoV-2 testing supplies and laboratory capacity now widely available in the United States, understanding the spatial heterogeneity of associations between social determinants and the use of SARS-CoV-2 testing is essential to improve testing availability in populations disproportionately affected by SARS-CoV-2. METHODS: We assessed positive and negative results of SARS-CoV-2 molecular tests conducted from February 1 through June 17, 2020, from the Massachusetts Virtual Epidemiologic Network, an integrated web-based surveillance and case management system in Massachusetts. Using geographically weighted regression and Moran's I spatial autocorrelation tests, we quantified the associations between SARS-CoV-2 testing rates and 11 metrics of the Social Vulnerability Index in all 351 towns in Massachusetts. RESULTS: Median SARS-CoV-2 testing rates decreased with increasing percentages of residents with limited English proficiency (median relative risk [interquartile range] = 0.96 [0.95-0.99]), residents aged ≥65 (0.97 [0.87-0.98]), residents without health insurance (0.96 [0.95-1.04], and people residing in crowded housing conditions (0.89 [0.80-0.94]). These associations differed spatially across Massachusetts, and localized models improved the explainable variation in SARS-CoV-2 testing rates by 8% to 12%. CONCLUSION: Indicators of social vulnerability are associated with variations in SARS-CoV-2 testing rates. Accounting for the spatial heterogeneity in these associations may improve the ability to explain and address the SARS-CoV-2 pandemic at substate levels.

Real-time surveillance for tuberculosis using electronic health record data from an ambulatory practice in eastern Massachusetts.

OBJECTIVE: Electronic health records (EHRs) have the potential to improve completeness and timeliness of tuberculosis (TB) surveillance relative to traditional reporting, particularly for culture-negative disease. We report on the development and validation of a TB detection algorithm for EHR data followed by implementation in a live surveillance and reporting system. METHODS: We used structured electronic data from an ambulatory practice in eastern Massachusetts to develop a screening algorithm aimed at achieving 100% sensitivity for confirmed active TB with the highest possible positive predictive value (PPV) for physician-suspected disease. We validated the algorithm in 16 years of retrospective electronic data and then implemented it in a real-time EHR-based surveillance system. We assessed PPV and the completeness of case capture relative to conventional reporting in 18 months of prospective surveillance. RESULTS: The final algorithm required a prescription for pyrazinamide, an International Classification of Diseases, Ninth Revision (ICD-9) code for TB and prescriptions for two antituberculous medications, or an ICD-9 code for TB and an order for a TB diagnostic test. During validation, this algorithm had a PPV of 84% (95% confidence interval 78, 88) for physician-suspected disease. One-third of confirmed cases were culture-negative. All false-positives were instances of latent TB. In 18 months of prospective EHR-based surveillance with this algorithm, seven additional cases of physician-suspected active TB were detected, including two patients with culture-negative disease. A review of state health department records revealed no cases missed by the algorithm. CONCLUSIONS: Live, prospective TB surveillance using EHR data is feasible and promising.

Electronic Support for Public Health: validated case finding and reporting for notifiable diseases using electronic medical data.

Health care providers are legally obliged to report cases of specified diseases to public health authorities, but existing manual, provider-initiated reporting systems generally result in incomplete, error-prone, and tardy information flow. Automated laboratory-based reports are more likely accurate and timely, but lack clinical information and treatment details. Here, we describe the Electronic Support for Public Health (ESP) application, a robust, automated, secure, portable public health detection and messaging system for cases of notifiable diseases. The ESP application applies disease specific logic to any complete source of electronic medical data in a fully automated process, and supports an optional case management workflow system for case notification control. All relevant clinical, laboratory and demographic details are securely transferred to the local health authority as an HL7 message. The ESP application has operated continuously in production mode since January 2007, applying rigorously validated case identification logic to ambulatory EMR data from more than 600,000 patients. Source code for this highly interoperable application is freely available under an approved open-source license at http://esphealth.org.

Using public health surveillance data to measure Clostridium difficile infection population burden in Massachusetts.

Clostridium difficile occurs both inside and outside of health care facilities, but surveillance has been traditionally limited to the hospital setting. To measure the population-based burden of C difficile infection (CDI), we used multiple routine sources of data. We found an overall rate of CDI in Massachusetts in 2016 of 132.5 per 100,000 population, with mortality in 2014 of 6.4 per 100,000 population. Population-based measurement of CDI burden appears feasible without conducting a special study.

Electronic Health Record Use in Public Health Infectious Disease Surveillance, USA, 2018-2019.

PURPOSE OF REVIEW: Electronic health records (EHRs) are an excellent source of data for disease symptoms, laboratory results, and medical treatments. Thus, EHR data may improve the completeness of notifiable disease case reporting and enable longitudinal collection of disease data. The purpose of this review is to examine the current state of EHR use in public health infectious disease surveillance in the USA. RECENT FINDINGS: A wide variety of EHR data is used in infectious disease surveillance. EHR data were used to assess the incidence of Lyme disease and identify newly diagnosed HIV infections. EHR disease detection algorithms combined laboratory reports, diagnosis codes, and medication orders to identify cases and, in the case of Lyme disease, found incidence rates 4-7 times higher than those from traditional surveillance. EHR data were also used to evaluate temporal trends in sexually transmitted disease testing, positivity, and re-testing in several primary care settings. Multiple studies were also able to control for additional confounders in multivariable models, such as number of sexual partners and concurrent infections, because of the breadth of data available in EHR systems. Studies highlighted in this review demonstrate that EHR data enhance provider-based and laboratory-based disease reports and may facilitate more complete case reporting. EHR data also provides corollary patient information that enables longitudinal disease reporting and analysis of important health outcomes. As public health infrastructure and investment allow health departments to establish closer relationships with healthcare providers, EHR data use in public health surveillance activities should continue to increase.

Building health IT capacity to improve HIV infection health outcomes.

Eighty-six percent of those engaged in HIV medical care in Massachusetts achieved viral suppression, making Massachusetts's long-term goal of eliminating new infections of HIV a real possibility. In order to achieve this goal, Massachusetts is working to engage all individuals living with HIV/AIDS in HIV medical care, keep them retained in care, and render their viral load non-detectable. Currently, in Massachusetts, the data elements necessary to monitor the HIV care continuum are documented in siloed health information systems that do not communicate with each other. Massachusetts has engaged in a pilot project to enhance their health information technology (IT) capacity to monitor the HIV care continuum and identify gaps in care. Massachusetts Virtual Epidemiologic Network (MAVEN) will be enhanced to perform as a consolidated electronic system to document and triage clinic-, laboratory-, and patient-level surveillance, field epidemiology and HIV care continuum data. The consolidation will enhance identification of patients infected with HIV and provide timely, actionable data for engagement and retention in HIV medical care.

Infectious disease surveillance in the 21st century: an integrated web-based surveillance and case management system.

The Massachusetts Virtual Epidemiologic Network (MAVEN) was deployed in 2006 by the Massachusetts Department of Public Health, Bureau of Infectious Disease to serve as an integrated, Web-based disease surveillance and case management system. MAVEN replaced program-specific, siloed databases, which were inaccessible to local public health and unable to integrate electronic reporting. Disease events are automatically created without human intervention when a case or laboratory report is received and triaged in real time to state and local public health personnel. Events move through workflows for initial notification, case investigation, and case management. Initial development was completed within 12 months and recent state regulations mandate the use of MAVEN by all 351 jurisdictions. More than 300 local boards of health are using MAVEN, there are approximately one million events, and 70 laboratories report electronically. MAVEN has demonstrated responsiveness and flexibility to emerging diseases while also streamlining routine surveillance processes and improving timeliness of notifications and data completeness, although the long-term resource requirements are significant.

Integrating clinical practice and public health surveillance using electronic medical record systems.

Electronic medical record (EMR) systems have rich potential to improve integration between primary care and the public health system at the point of care. EMRs make it possible for clinicians to contribute timely, clinically detailed surveillance data to public health practitioners without changing their existing workflows or incurring extra work. New surveillance systems can extract raw data from providers' EMRs, analyze them for conditions of public health interest, and automatically communicate results to health departments. The current paper describes a model EMR-based public health surveillance platform called Electronic Medical Record Support for Public Health (ESP). The ESP platform provides live, automated surveillance for notifiable diseases, influenza-like illness, and diabetes prevalence, care, and complications. Results are automatically transmitted to state health departments.

Automated identification of acute hepatitis B using electronic medical record data to facilitate public health surveillance.

BACKGROUND: Automatic identification of notifiable diseases from electronic medical records can potentially improve the timeliness and completeness of public health surveillance. We describe the development and implementation of an algorithm for prospective surveillance of patients with acute hepatitis B using electronic medical record data. METHODS: Initial algorithms were created by adapting Centers for Disease Control and Prevention diagnostic criteria for acute hepatitis B into electronic terms. The algorithms were tested by applying them to ambulatory electronic medical record data spanning 1990 to May 2006. A physician reviewer classified each case identified as acute or chronic infection. Additional criteria were added to algorithms in serial fashion to improve accuracy. The best algorithm was validated by applying it to prospective electronic medical record data from June 2006 through April 2008. Completeness of case capture was assessed by comparison with state health department records. FINDINGS: A final algorithm including a positive hepatitis B specific test, elevated transaminases and bilirubin, absence of prior positive hepatitis B tests, and absence of an ICD9 code for chronic hepatitis B identified 112/113 patients with acute hepatitis B (sensitivity 97.4%, 95% confidence interval 94-100%; specificity 93.8%, 95% confidence interval 87-100%). Application of this algorithm to prospective electronic medical record data identified 8 cases without false positives. These included 4 patients that had not been reported to the health department. There were no known cases of acute hepatitis B missed by the algorithm. CONCLUSIONS: An algorithm using codified electronic medical record data can reliably detect acute hepatitis B. The completeness of public health surveillance may be improved by automatically identifying notifiable diseases from electronic medical record data.