RiskScape: A Data Visualization and Aggregation Platform for Public Health Surveillance Using Routine Electronic Health Record Data.

Automated analysis of electronic health record (EHR) data is a complementary tool for public health surveillance. Analyzing and presenting these data, however, demands new methods of data communication optimized to the detail, flexibility, and timeliness of EHR data.RiskScape is an open-source, interactive, Web-based, user-friendly data aggregation and visualization platform for public health surveillance using EHR data. RiskScape displays near-real-time surveillance data and enables clinical practices and health departments to review, analyze, map, and trend aggregate data on chronic conditions and infectious diseases. Data presentations include heat maps of prevalence by zip code, time series with statistics for trends, and care cascades for conditions such as HIV and HCV. The platform's flexibility enables it to be modified to incorporate new conditions quickly-such as COVID-19.The Massachusetts Department of Public Health (MDPH) uses RiskScape to monitor conditions of interest using data that are updated monthly from clinical practice groups that cover approximately 20% of the state population. RiskScape serves an essential role in demonstrating need and burden for MDPH's applications for funding, particularly through the identification of inequitably burdened populations.

More Screening or More Disease? Gonorrhea Testing and Positivity Patterns Among Men in 3 Large Clinical Practices in Massachusetts, 2010-2017.

BACKGROUND: Gonorrhea diagnosis rates in the United States increased by 75% during 2009-2017, predominantly in men. It is unclear whether the increase among men is being driven by more screening, an increase in the prevalence of disease, or both. We sought to evaluate changes in gonorrhea testing patterns and positivity among men in Massachusetts. METHODS: The analysis included men (aged ≥15 years) who received care during 2010-2017 in 3 clinical practice groups. We calculated annual percentages of men with ≥1 gonorrhea test and men with ≥1 positive result, among men tested. Log-binomial regression models were used to examine trends in these outcomes. We adjusted for clinical and demographic characteristics that may influence the predilection to test and probability of gonorrhea disease. RESULTS: On average, 306 348 men had clinical encounters each year. There was a significant increase in men with ≥1 gonorrhea test from 2010 (3.1%) to 2017 (6.4%; adjusted annual risk ratio, 1.12; 95% confidence interval, 1.12-1.13). There was a significant, albeit lesser, increase in the percentage of tested men with ≥1 positive result (1.0% in 2010 to 1.5% in 2017; adjusted annual risk ratio, 1.07; 95% confidence interval, 1.04-1.09). CONCLUSIONS: We estimated significant increases in the annual percentages of men with ≥1 gonorrhea test and men with ≥1 positive gonorrhea test result between 2010 and 2017. These results suggest that observed increases in gonorrhea rates could be explained by both increases in screening and the prevalence of gonorrhea.

Temporal Patterns in Chlamydia Repeat Testing in Massachusetts.

INTRODUCTION: National guidelines recommend test-of-cure for pregnant women and test-of-reinfection for all patients with chlamydia infections in order to interrupt transmission and prevent adverse sequelae for patients, partners, and newborns. Little is known about retesting and positivity rates, and whether they are changing over time, particularly in private sector practices. METHODS: Electronic health record data on patients with chlamydia tests were extracted from three independent clinical practice groups serving ≅20% of the Massachusetts population. Records were extracted using the Electronic medical record Support for Public Health platform (esphealth.org). These data were analyzed for temporal trends in annual repeat testing rates by using generalized estimating equations after index positive chlamydia tests between 2010 and 2015 and for differences in intervals to first repeat tests among pregnant females, non-pregnant females, and males. Data extraction and analysis were performed during calendar years 2017 and 2018. RESULTS: An index positive C. trachomatis result was identified for 972 pregnant female cases, 10,309 non-pregnant female cases, and 4,973 male cases. Test-of-cure 3-5 weeks after an index positive test occurred in 37% of pregnant females. Test-of-reinfection 8-16 weeks after an index positive test occurred in 39% of pregnant females, 18% of non-pregnant females, and 9% of males. There were no significant increases in test-of-cure or test-of-reinfection rates from 2010 to 2015. Among cases with repeat tests, 16% of pregnant females, 15% of non-pregnant females, and 16% of males had positive results. CONCLUSIONS: Chlamydia test-of-cure and test-of-reinfection rates are low, with no evidence of improvement over time. There are substantial opportunities to improve adherence to chlamydia repeat testing recommendations.

State and Local Chronic Disease Surveillance Using Electronic Health Record Systems.

OBJECTIVES: To assess the feasibility of chronic disease surveillance using distributed analysis of electronic health records and to compare results with Behavioral Risk Factor Surveillance System (BRFSS) state and small-area estimates. METHODS: We queried the electronic health records of 3 independent Massachusetts-based practice groups using a distributed analysis tool called MDPHnet to measure the prevalence of diabetes, asthma, smoking, hypertension, and obesity in adults for the state and 13 cities. We adjusted observed rates for age, gender, and race/ethnicity relative to census data and compared them with BRFSS state and small-area estimates. RESULTS: The MDPHnet population under surveillance included 1 073 545 adults (21.8% of the state adult population). MDPHnet and BRFSS state-level estimates were similar: 9.4% versus 9.7% for diabetes, 10.0% versus 12.0% for asthma, 13.5% versus 14.7% for smoking, 26.3% versus 29.6% for hypertension, and 22.8% versus 23.8% for obesity. Correlation coefficients for MDPHnet versus BRFSS small-area estimates ranged from 0.890 for diabetes to 0.646 for obesity. CONCLUSIONS: Chronic disease surveillance using electronic health record data is feasible and generates estimates comparable with BRFSS state and small-area estimates.