Estimating the Recurrence Rate of Gestational Diabetes Mellitus (GDM) in Massachusetts 1998-2007: Methods and Findings.

OBJECTIVES: Women with gestational diabetes mellitus (GDM) may be able to reduce their risk of recurrent GDM and progression to type 2 diabetes mellitus through lifestyle change; however, there is limited population-based information on GDM recurrence rates. METHODS: We used data from a population of women delivering two sequential live singleton infants in Massachusetts (1998-2007) to estimate the prevalence of chronic diabetes mellitus (CDM) and GDM in parity one pregnancies and recurrence of GDM and progression from GDM to CDM in parity two pregnancies. We examined four diabetes classification approaches; birth certificate (BC) data alone, hospital discharge (HD) data alone, both sources hierarchically combined with a diagnosis of CDM from either source taking priority over a diagnosis of GDM, and both sources combined including only pregnancies with full agreement in diagnosis. Descriptive statistics were used to describe population characteristics, prevalence of CDM and GDM, and recurrence of diabetes in successive pregnancies. Diabetes classification agreement was assessed using the Kappa statistic. Associated maternal characteristics were examined through adjusted model-based t tests and Chi square tests. RESULTS: A total of 134,670 women with two sequential deliveries of parities one and two were identified. While there was only slight agreement on GDM classification across HD and BC records, estimates of GDM recurrence were fairly consistent; nearly half of women with GDM in their parity one pregnancy developed GDM in their subsequent pregnancy. While estimates of progression from GDM to CDM across sequential pregnancies were more variable, all approaches yielded estimates of ≤5 %. The development of either GDM or CDM following a parity one pregnancy with no diagnosis of diabetes was <3 % across approaches. Women with recurrent GDM were disproportionately older and foreign born. CONCLUSION: Recurrent GDM is a serious life course public health issue; the inter-pregnancy interval provides an important window for diabetes prevention.

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.

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.

Harnessing electronic health records for public health surveillance.

Electronic medical record (EMR) systems are a rich potential source for detailed, timely, and efficient surveillance of large populations. We created the Electronic medical record Support for Public Health (ESP) system to facilitate and demonstrate the potential advantages of harnessing EMRs for public health surveillance. ESP organizes and analyzes EMR data for events of public health interest and transmits electronic case reports or aggregate population summaries to public health agencies as appropriate. It is designed to be compatible with any EMR system and can be customized to different states' messaging requirements. All ESP code is open source and freely available. ESP currently has modules for notifiable disease, influenza-like illness syndrome, and diabetes surveillance. An intelligent presentation system for ESP called the RiskScape is under development. The RiskScape displays surveillance data in an accessible and intelligible format by automatically mapping results by zip code, stratifying outcomes by demographic and clinical parameters, and enabling users to specify custom queries and stratifications. The goal of RiskScape is to provide public health practitioners with rich, up-to-date views of health measures that facilitate timely identification of health disparities and opportunities for targeted interventions. ESP installations are currently operational in Massachusetts and Ohio, providing live, automated surveillance on over 1 million patients. Additional installations are underway at two more large practices in Massachusetts.