Category: Data Analytics

Strategic Skill Area

Mortality surveillance using an Automated Mortality Syndromic Surveillance System (MortalSS) – Lessons Learned from New York City

Course Objective

Identify aberrations in time-series data.
Classify free text cause of death data into multiple ICD-10 code categories using Natural Language Processing (NLP).
Construct interactive html dashboards in R and RStudio.

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Date: February 7th, 2023

Presenter:
Alejandro F. Castro III, MPH
Mortality Surveillance Analyst,
New York City Department of Health and Mental Hygiene (NYCDOHMH)

In this month’s Log-in2Learn, Alejandro Castro III presents on mortality surveillance using an Automated Mortality Syndromic Surveillance System (MortalSS). He begins by explaining the importance of mortality surveillance in New York City and reviews the death registration process. He goes on to explain MortalSS, including time series analysis, model implementation, and interactive dashboards. He then discusses how the COVID-19 pandemic impacted mortality surveillance in New York City and the potential future impact of MortalSS. Castro ends by explaining artificial intelligence (AI) and machine learning (ML) for ICD-10 cause of death coding.

Participants will be able to:

Identify aberrations in time-series data.
Classify free text cause of death data into multiple ICD-10 code categories using Natural Language Processing (NLP).
Construct interactive html dashboards in R and RStudio.

The Purpose of Pilot Studies in Modern Research

Course Objective

Describe the cons of estimating effect sizes from pilot studies.
Contrast the cons of using pilot studies for power computations with pros of using the clinically meaningful estimate.
Describe the purpose of pilot studies in modern research.

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Date: January 8, 2019

Presenter:
Martina Pavlicova, PhD, MS
Associate Professor of Biostatistics
Columbia University Medical Center

In this webinar, participants learn from Dr. Martina Pavlicova about the benefits and limitations of pilot testing in clinical research. First, Dr. Pavlicova uses a case study to provide a comprehensive review on hypothesis testing, random sampling, and data stratification. Since effect size and sample size effect power, participants learn that pilot studies have limited statistical significance. Dr. Pavlicova explains how piloting is still essential to clinical research when determining feasibility, acceptability, safety, and tolerability of a study.

Introduction to Qualitative Analysis with ATLAS.ti

Course Objective

Critically interpret the meaning of textual data using inductive reasoning
Develop a preliminary classification scheme using interpretative reading
Categorize inferences with meaningful conceptual labels and/or codes
Formulate conclusions based on relationships between established categories and/or codes
Perform basic qualitative analysis techniques in ATLAS.ti software, including:
- Prepare primary documents for importing into ATLAS.ti’s Hermeneutic Unit
- Generate output of coded textual data
- Troubleshoot key importing, coding and output operations in ATLAS.ti

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Date: December 14, 2018

Presenter:
New York City-Long Island-Lower Tri-County Public Health Training Center with revisions made by the Region 2 Public Health Training Center

Qualitative research produces rich, narrative data that requires both analysis and interpretation. In this section, learners are guided through the basic steps of the analysis process: Organize, Reduce, and Describe. An interactive practice exercise accompanies each step. Following this discussion, ATLAS.ti is introduced as a computer assisted software package that can supplement and improve pen and paper coding processes. Users follow instructional videos to learn how to use ATLAS.ti to manage large bodies of textual, graphical, audio, and video data. It is recommended that users download a trial version of ATLAS.ti software to follow along with the instructional videos in Part III and to practice at home. Download a trial version of ATLAS.ti.

Public Media Data for Public Health

Course Objective

Describe public media data available for disease surveillance
Describe public media data available for audience segmentation
Describe public media data available for message design and tailoring

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Date: July 10, 2018

Presenter:
Dr. Joe Smyser, PhD
CEO
Public Good Projects

This webinar explores new ways to use public media data to solve large, complex public heatlh issues like opioid abuse and mental health. Dr. Joe Smyser explains how the Public Good Projects uses data from Facebook, Google, and designated market areas (DMAs) to create insights about a population’s health knowledge, attitudes, and beliefs in real time to inform public health media campaigns. Participants of this webinar will be exposed to case studies of how this data was used to create tailored messages for specific populations about opioids and mental health using digital marketing principles.

The City Health Dashboard: A New Resource for Population Health Improvement

Course Objective

Describe the role of data in improving population health in urban areas
Describe how data on health status and health determinants improve cross-sector collaboration and decision making around health
Explain how the City Health Dashboard can be improved to be a more effective tool for health improvement

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Date: May 1, 2018

Presenter:
Shoshanna Levine, MPH, DrPH
Program Director
City Health Dashboard

Over two-thirds of the U.S. population lives in cities. There is currently a shift for city governments to work with multi-level stakeholders to use a population health approach to target social determinants of health and improve the overall quality and health of the population. Dr. Soshanna Levine discusses the importance of using data as a cross-sectional, collaborative health improvement approach. The Department of Population Health at NYU Langone Medical Center and the Robert F. Wagner School of Public Service at NYU partnered with national networks to create the City Health Dashboard to help cities understand, compare, and take action to improve the health of their municipalities. The tool uses data from federal, state, and local agencies to present 36 measures linked to the health status across five domains (health behaviors, clinical care, social and economic factors, physical environment, health outcomes). The dashboard is a health improvement planning resource for 500 cities across the U.S. and will also provide evidence-based interventions and resources to city leadership, government, and stakeholders. Dr. Levine presents an overview of the dashboard and methods to engage local communities in data-driven health improvement activities.

Using Geographic Information Science to Advance Health Equity and Environmental Justice

Course Objective

To describe how geographic information science can be used to advance health equity and environmental justice.
To describe the environmental factors that lead to health disparities.
To list examples of how geographic information science has been used in health equity research.

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Date: May 2, 2017

Presenter:
Andrew Maroko, PhD
Associate Director
Lehman College Urban GISc Lab
CUNY Graduate School of Public Health and Health Policy

Environmental factors have an important impact on the health of communities. Public health professionals may use geographic information sciences (GIS) to assess the health of communities by analyzing exposure, or being subjected to negative factors such as pollution, as well as accessibility, or the ability to access positive factors such as green space and healthy food. In this webinar, Dr. Andrew Maroko discusses the process of geovisualization, hypothesis generation, data exploration, and communication and knowledge transfer in conducting environmental justice research. Dr. Maroko also describes various methods and technologies used to estimate exposure and accessibility, and provides examples of GIS in environmental justice/health equity projects in New York City and Glasgow, Scotland.