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Surveillance of Spina Bifida across the Lifespan

Sector: Hospital • Location: United States of America

Source: Grants.gov

Project
Forecasted

Synopsis: CDC-RFA-DD-24-0065 solicits non-research, cooperative agreement applications to conduct a project to identify a population-based sample of children and adults with spina bifida to estimate the prevalence and describe key outcomes with the goal of identifying opportunities to improve the health of individuals living with spina bifida. The population included in this surveillance activity

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The project “Surveillance of Spina Bifida across the Lifespan” is an infrastructure initiative in the Hospital sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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forecasted

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Description

Description

Synopsis: CDC-RFA-DD-24-0065 solicits non-research, cooperative agreement applications to conduct a project to identify a population-based sample of children and adults with spina bifida to estimate the prevalence and describe key outcomes with the goal of identifying opportunities to improve the health of individuals living with spina bifida. The population included in this surveillance activity can be an entire state or a region within a state. Individuals with spina bifida should be identified through existing data sources including the state birth defects surveillance program or vital records and long term health outcomes should be assessed using sources such as electronic health records, administrative data (e.g., Medicaid/Medicare, hospital discharge), or other sources available to funding recipients. This project will involve a required component (A) and an optional component (B). The total estimated funding for Component A is $8,000,000 for 5 years. The total estimated funding for Component B is $400,000 for 2 years (e.g., years 2 and 3 only) and will be subject to available funding.Background: Spina bifida is one of the most common types of structural birth defects with approximately 1,400 children born with spina bifida each year in the United States (U.S.). Current estimates for the U.S. include a greater number of adults living with spina bifida than children. Conducting population-based surveillance of children and adults with spina bifida is challenging. Birth defects surveillance systems identify babies born with a birth defect but do not follow them as children and adults. The National Spina Bifida Patient Registry (NSBPR) provides valuable longitudinal information on the clinical care provided by spina bifida clinics but lacks information from other clinical settings and from the overall population of individuals with spina bifida. Standard survey methods, such as those that sample households, would not identify enough children and adults with spina bifida to produce reliable estimates and description of the population. Therefore, the objective of this surveillance project is to conduct population-based surveillance of children and adults with spina bifida by combining information from multiple sources (e.g., state birth defects surveillance systems, vital records, electronic health records, administrative data). Through this surveillance activity, it would be possible to assess their health status and healthcare use.Objectives: Component A objectives include: (1) estimate prevalence of spina bifida across all ages, along with mortality and survival; (2) describe healthcare utilization and sources of care; and (3) assess health status and long-term outcomes. The surveillance area can be an entire state or a region within in a state, with the applicant utilizing multiple data sources to generate an accurate and complete population-based sample. Required data sources include population-based birth defects surveillance systems (e.g., state birth defects surveillance), vital records, and sources for longitudinal follow-up (e.g., electronic health records, administrative data [e.g., Medicaid/Medicare; hospital discharge]). The objective of Component B will be to develop and test novel approaches to case-finding, reporting, or outcome prediction (e.g., machine learning algorithms, natural language processing).

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Source reliability

High

Data quality score

100%

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