Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation

Abstract

Background: Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations.

Methods: Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings.

Results: By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis.

Conclusion: While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.

Keywords: Infant health; Maternal health; Public health informatics; Social determinants of health; Surveillance systems.

Fig. 1

Vision for the Ohio Equity Initiative data collection infrastructure

Fig. 2

Data Collection Workflow: The CBO collects information on its participant that includes all the required data elements. It maintains those records in digital form. It is able to extract data on those records digitally in .CSV, .XLS, .XLSX, or any other Excel-readable file. Organizational Workflow: The CBO extracts a dataset and submits those data through either email or a web portal in Qualtrics

Fig. 3

Integration Workflow: The research team downloads data from email or Qualtrics. Upon the research team’s receipt of the data, the files are preprocessed to ensure they conform to data standards. The data are appended to the Data Repository

Fig. 4

Data Collection Workflow: The CBO collects information on its participant that includes all the required data elements. It maintains those records in digital form. It is able to extract data on those records digitally in .CSV, .XLS, .XLSX, or any other Excel-readable file. Organizational Workflow: The CBO extracts a dataset and submits those data through either email or a web portal in Qualtrics

Fig. 5

Integration Workflow: The research team downloads data from email or Qualtrics. Upon the research team’s receipt of the data, the files are preprocessed to ensure that they conform to data standards. The research team uses a transformation script that maps the provided data transfer to the Data Model Specification. The data are appended to the Data Repository

Fig. 6

Data Collection Workflow: The CBO elects to use direct web entry of data at the point of service. Organizational Workflow: The CBO logs into the Data Collection website and completes the required forms for any participant based on point of contact. Integration Workflow: The research team uses the repository application programming interface (API) to transfer data into the data repository

Fig. 7

Data Collection Workflow: The CBO collects information on its participant that includes all the required data elements. It maintains those records in digital form (applicable to case 4A). It is able to fax, scan, or mail records to Ohio State (applicable to case 4B). It is able to extract data on those records digitally in .CSV, .XLS, .XLSX, or any other Excel-readable file. Organizational Workflow: The CBO submits those data through fax, email, or mail. Integration Workflow: Upon the research team’s receipt of the data, the files are preprocessed to ensure they conform to data standards. The research team uses a transformation script that maps the provided data transfer to the Data Model Specification. The data are appended to the Data Repository

Fig. 8

This image from the OEI dashboard shows enrollment statistics for participating CBOs with details based on race, enrollment date, and other variables collected by the CBOs