Edit Your Search

Level:

Accessible:

Continuing Education:


New Search

Search Results

Search Results

Displaying records 1 through 10 of 10 found.

Training Course in Maternal and Child Healthh (MCH) Epidemiology. Year Developed: 2018. Source: CityMATCH. Presenter(s): n.a.. Type: Webinar Series. Level: Introductory Intermediate Advanced. Length: Series; various lengths.

Annotation: This national program is aimed primarily at professionals in state and local health agencies who have significant responsibility for collecting, processing, analyzing, and reporting Maternal And Child Health data. On different years the course is geared to individuals with beginning to intermediate or intermediate to advanced skills in statistical and epidemiologic methods, preferably in MCH or a related field.

Staying Ahead of the Curve: Modeling and Public Health Decision-Making. Year Developed: 2016. Source: Centers for Disease Control-Public Health Grand Rounds. Presenter(s): Richard Hatchett, MD; Daniel Jernigan, MD; Martin Meltzer, PhD; Lauren Ancel Meyers, PhD . Type: Video Webinar. Level: Intermediate. Length: 60 minutes.

Annotation: Where are infections spreading? How many people will be affected? What are some different ways to stop the spread of an epidemic? In a process known as modeling, scientists analyze data using complex mathematical methods to provide answers to these and other questions during an emergency response. Models provide the foresight that can help decision makers better prepare for the future.

Learning Objectives:

Statistical and Epidemiological Framework for Public Health Analyses. Year Developed: 2015. Source: Health Resources and Services Administration, Maternal and Child Health Bureau; Centers for Disease Control and Prevention; CityMatCH. Presenter(s): Deb Rosenberg, PhD. Type: Narrated Slide Presentation. Level: Intermediate Advanced. Length: 77 slides. Audio

Annotation: This pre-course webinar for the 2015 Training Course in MCH Epidemiology provides an overview of MCH epidemiology including the basics of analytic methods, the sampling framework, and the epidemiologic framework.

Multivariable Approaches for Supporting the MCH Planning Cycle I: Stratified Analysis and Bias. Year Developed: 2015. Source: CityMatCH. Presenter(s): Deb Rosenberg, PhD. Type: Narrated Slide Presentation. Level: Advanced. Length: 25 slides. Slides

Annotation: This webinar for the 2015 Training Course in MCH Epidemiology discusses bias and confounding, confounding and effect modification, the example of ACES and mental health, stratified analysis, and linear models.

2015 Training Course in MCH Epidemiology. Year Developed: 2015. Source: Health Resources and Services Administration, Maternal and Child Health Bureau; Centers for Disease Control and Prevention; CityMatCH. Presenter(s): n.a.. Type: Conference Archive. Level: Introductory Intermediate Advanced. Length: Series; various lengths.

Annotation: This resource provides an archive of the 2015 Training Course in MCH Epidemiology, including pre-course webinars, on-site sessions, and a post-conference webinars. It includes slides for each presentation plus audio files. Each presentation is also entered separately into the course database.

Measurement Selection and Development for Maternal and Child Health Research. Year Developed: 2012. Source: U.S. Maternal and Child Health Bureau, Office of Epidemiology and Research. Presenter(s): James Guevara, MD, MPH; Carole Tucker, PT, PhD. Type: Webinar Archive. Level: Intermediate. Length: 60 minutes.

Annotation: This webinar—hosted by the Maternal and Child Health Extramural Research Program (EnRich)—focuses on best practices and methods for the selection of MCH research measurement instruments. Dr. Guevara begins by defining types of health measures and how to use the health belief model to inform practice. He continues by addressing reliability, validity, and validation of measures. Next, Dr. Tucker covers the development, such as the purpose, population, outcome, granularity, and administration mode, of health measures. She further describes the purpose, advantages and limitations classical measurement approaches, Computer Adaptive Testing (CAT), and Item Response Theory (IRT). A question and answer session conclude the presentation.

Learning Objectives: • To determine best practices for the selection of measurement instruments in maternal-child health research. • To understand methods for measure development in maternal-child health research.

Using GIS in MCH Research and Planning. Year Developed: 2008. Source: U.S. Maternal and Child Health Bureau. Presenter(s): Russell S. Kirby, PhD, MS, FACE; Sara McLafferty, PhD; Eugene R. Takahashi, PhD, MPH. Type: Webinar Archive. Level: Intermediate. Length: 60 minutes.

Annotation: In this introduction to Geographic Information Systems (GIS), Drs. Russell Kirby, Sara McLafferty, and Eugene Takahashi discuss applications of GIS to maternal and child health. Dr. Kirby begins with a definition of geo-coding, its methodology, and the types of maps that are used in public health settings. Dr. McLafferty continues by discussing an example of how geocoding was used to analyze access to prenatal care among immigrants in New York City. Dr. Takahashi concludes the webinar by describing the use of GIS to map teen birth rates in California in order to target resources for prevention efforts.

Learning Objectives: • Learn a basic understanding of GIS. • Describe examples of how GIS has been used to examine variation in maternal and child health outcomes. • Understand how GIS has been used to map teen birth rates by geographic area, race and ethnicity.

Special Instructions: DataSpeak uses a number of different technologies. To get the most out of the information, please review the technical requirements at http://hrsa.gov/archive/mchb/dataspeak/techreq/index.html

Contextual Analysis, Part 3: Applying the Statistical Concepts to Real Data. Year Developed: 2007. Source: U.S. Maternal and Child Health Bureau. Presenter(s): Patricia O’Campo, PhD. Type: Webinar Archive. Level: Intermediate Advanced. Length: 60 minutes.

Annotation: Dr. Patricia O’Campo of the University of Toronto discusses contextual analysis in her study of neighborhood deprivation and preterm birth rates; her multi-site study of the effects of neighborhood deprivation was conducted in rural and urban areas. She begins with a description of how she constructed an index of neighborhood deprivation, and continues with an overview of her methods of data analysis. Dr. O’Campo concludes with a discussion of the study’s results, and presents additional resources on multilevel modeling. A question and answer session follow.

Learning Objectives: • How was an index of neighborhood deprivation developed? • Is the Neighborhood Deprivation Index associated with Preterm Birth in a single site? • Is the Neighborhood Deprivation Index associated with Preterm Birth across multiple sites? • What are some resources available for those wanting to learn more about multilevel modeling?

Special Instructions: DataSpeak uses a number of different technologies. To get the most out of the information, please review the technical requirements at http://hrsa.gov/archive/mchb/dataspeak/techreq/index.html

Contextual Analysis, Part 2: Methods for Understanding & Interpreting Multilevel Analysis. Year Developed: 2007. Source: U.S. Maternal and Child Health Bureau. Presenter(s): Michael Kogan, PhD; Jay Kaufman, PhD. Type: Webinar Archive. Level: Intermediate Advanced. Length: 60 minutes.

Annotation: This DataSpeak program is the second in a three-part series on the use of contextual analysis, an approach for assessing the effect of contextual, or neighborhood, characteristics along with individual-level factors in explaining disparities in health outcomes. Each program in the series features one of several university-based researchers funded by the Maternal and Child Health Bureau (MCHB) in the Health Resources and Services Administration to explore the effect of neighborhoods on our country’s relatively high infant mortality rate as compared to other industrialized countries and wide racial disparities in infant mortality and preterm birth. This series is intended to provide public health professionals with background and knowledge of concepts and statistical analysis techniques to begin developing and adapting the approach to their specific States and communities. The first program in the series, broadcast on May 16th, presented an overview of contextual analysis, including discussion of how neighborhoods are defined, what sources of data are available at the neighborhood level, and how neighborhood conditions can affect health. This second program will describe several different multilevel analysis techniques, the advantages and disadvantages of these different approaches, examples of their use for the analysis of preterm birth data, and the interpretation of statistical results.

Learning Objectives: • Understand the terminology used when describing multilevel models. • Understand the use of random-effect models, as well as mixed models that include random and fixed effects. • Learn how to interpret various multilevel models.

Special Instructions: DataSpeak uses a number of different technologies. To get the most out of the information, please review the technical requirements at http://hrsa.gov/archive/mchb/dataspeak/techreq/index.html

Contextual Analysis, Part 1: A Tool for Understanding Disparities in Preterm Birth. Year Developed: 2007. Source: U.S. Maternal and Child Health Bureau. Presenter(s): Mary Kay Kenney, PhD; Jennifer Culhane, MPH, PhD. Type: Webinar Archive. Level: Intermediate. Length: 75 minutes.

Annotation: In this introduction to contextual analysis, the presenters discuss contextual (neighborhood) factors associated with preterm birth. Dr. Kenney begins by describing the concept of neighborhoods influencing health status, and the importance of understanding these factors as they relate to preterm birth and health disparities. Dr. Culhane continues by providing a background of contextual analysis, and explains how it relates to individual risk factors, and how to incorporate neighborhood factors into models of risk behaviors and health outcomes.

Learning Objectives: • Discuss and overview of contextual analysis. • Discuss how neighborhoods are defined. • Learn what sources of data are available at the neighborhood level. • Describe how neighborhood conditions can affect health.

Special Instructions: DataSpeak uses a number of different technologies. To get the most out of the information, please review the technical requirements at http://hrsa.gov/archive/mchb/dataspeak/techreq/index.html

New Search

This project is supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under grant number UE8MC25742; MCH Navigator for $180,000/year. This information or content and conclusions are those of the author and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government.