NSSP Update – June 2019

People

Community of Practice

NSSP CoP Cooperative Agreement is Transitioning to a New Partner

For almost three years, the International Society for Disease Surveillance (ISDS) has facilitated the National Syndromic Surveillance Program Community of Practice (NSSP CoP). ISDS—a leader in health informatics and preparedness activities since 2005—has played a critical role in advancing the science and practice of health surveillance worldwide. Thanks to the ISDS body of work, its facilitation of the NSSP CoP, and the hard work of many dedicated experts in syndromic surveillance, NSSP has a strong, sustainable community of practice.

On May 9, 2019, ISDS and CDC NSSP notified community members that the current cooperative agreement between CDC and ISDS that supports the work of the CoP will end June 30, 2019. It will be replaced with a new partner cooperative agreement being competed through CDC’s Center for State, Tribal, Local, and Territorial Support National Partnership (CSTLTS) cooperative agreement.

As we transition from one cooperative agreement to another, we encourage you to continue your membership in the NSSP CoP and to engage in ongoing community activities. Popular ways to interact with your peers—including workgroups, committees, and monthly NSSP CoP call—will sharpen your surveillance skills and provide valuable sources of information.

Please be patient with us as we work through the transition. Syndromic surveillance is an essential part of CDC’s Strategic Surveillance Strategy, and we will continue supporting it and the NSSP CoP throughout the transition and beyond.

NSSP Community of Practice Call

Please join the monthly NSSP CoP call. This call is powered by community members who share guidance, resources, and technical assistance. The call includes a forum for discussion and questions. The next call will be held on June 18, 2019, 3:00–4:30 PM ET. The discussion topic is Heat-related Illness Surveillance.

NSSP Community of Practice Article Published in Public Health Reports

The online May/June 2019 issue of Public Health Reports includes an article about the NSSP Community of Practice: “Building State and Local Public Health Capacity in Syndromic Surveillance Through an Online Community of Practice.” The article was written by Deborah W. Gould, Emilie Lamb, Shandy Dearth, and Krystal Collier. The authors describe the NSSP CoP’s design, membership, activities, and opportunities to use collective knowledge to build syndromic surveillance. (The article is this month’s featured “spotlight article.”)

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Implementation Guide for Syndromic Surveillance—New Milestones and Dates!

* Milestones revised May 20, 2019.
** Version 2.0 is currently being used; subsequent versions are working drafts only.
Shaded activities have been completed.

CDC Funding Recipients and Partnership Updates

North Carolina Integrates DMAT with NSSP Data

NSSP can quickly integrate data from DMATs whenever they are deployed. Other public health departments might want to update their emergency response plans to include the potential use of this data stream. This story also describes how the North Carolina DPH made its data available graphically and for analyses via data visualizations and dashboards.

Content was provided by Zachary Faigen, the lead for the NC DETECT program at the North Carolina Department of Health and Human Services. Faigen was one of the presenters for the April webinar “Data Sharing through Dashboards: The Who, What, Where, When and Why,” posted in the Knowledge Repository. During this lightening talk webinar, he describes the value of the North Carolina Disease Data Dashboard.

Please Respond to the NSSP BioSense Platform User Assessment

If you’ve used the BioSense Platform in recent months, you most likely received an email inviting you to take part in the NSSP BioSense Platform User Assessment. This assessment evaluates the utility and functionality of the NSSP’s BioSense Platform—from tool performance to Quick Start Guide usefulness and NSSP support.

Your feedback is very important to us. The BioSense Platform is continually enhanced on the basis of user feedback.

The assessment will close June 13, 2019. Participation is voluntary, but we strongly encourage you to provide us with your thoughts and insight. If you have questions or comments about the assessment, please contact the NSSP mailbox at nssp@cdc.gov and select User Feedback-General Feedback or Hussain Yusuf, team lead for the Program Evaluation Team, at hay0@cdc.gov.

ESSENCE QUERIES

Query Terms from April 2019

A key feature of NSSP–ESSENCE is its ability to use free-text definitions across different fields to generate custom queries. The SyS community can use this feature to develop and refine category definitions and to identify and respond to public health events that may not be apparent in established categorical definitions (i.e., syndromes, subsyndromes, and Chief Complaint and Discharge Diagnosis [CCDD] categories).

This word cloud summarizes free-text and coded values that epidemiologists and data analysts queried in ESSENCE during April 2019. This word cloud was generated using the Wordcloud2 package in RStudio. The relative size of the terms suggest the frequency of use in a query. NSSP super administrators can query ESSENCE usage logs to learn what queries are run nationwide and how long they take to complete.

Do you enjoy seeing national-level data depicted in a word cloud? Let us know!

Practice

Free-text Coding in NSSP–ESSENCE

Part 2. Underscores_and Brackets [ ]

Underscores and Brackets are alike in that each represents a single position in a string of numbers, text, or symbols. An Underscore is a placeholder that specifies that something, anything at all, must be in a particular position. A Bracket allows the user to specify what options can appear in that position.

_ Underscores
A placeholder indicating that something must be present in the position.

Let’s assume the following Chief Complaints (CC), previously used in Part 1 Wildcards, and a desire to create a query for fall-related injuries.

1. Fall
2. Fell getting out of car
3. Left arm injury; Fall
4. Falling out with friends; Suicidal
5. Feels crestfallen
6. Patient brought in after falling on face
7. Fall; Left wrist injury
8. Feels congested; Allergies

You may reasonably assume the boldfaced CCs 1, 2, 3, 6, and 7 are the intended cases and 4, 5, and 8 are false positives.

Here’s a table that shows how the use of the _Underscore can affect results:

 

Notes on How to Use Underscores in Queries

• To query the Underscore symbol, place it in Brackets: ^[_]^
• Underscores are useful in ICD10-CM queries when the query coder cares about what’s in later digits of the code but does not need to specify what comes before it.

In the following example, the two Underscores in a row indicate that in this definition the “2” in the sixth place indicates intentional self-harm and is the important part of that diagnosis code: ^T39.__2^

It is is common practice to include decimal and non-decimal forms of ICD10 codes in syndrome definitions and free-text code. Use caution when doing this.

If we delete the decimal in the ^T39.__2^ example to create a non-decimal query ^T39__2^, we would pick up the intended non-decimal form ICD10 codes like T39012A and T392X2A—BUT, the query would also capture unintended codes like the ICD10 codes T39.121A or T39.829A. Luckily, this isn’t relevant for our “intentional self-harm” example because there are no valid T39 ICD10 codes with a 2 in the fifth place, but this isn’t true for all codes and could result in the inclusion of unintended visits in your query.

[ ] Brackets
Placeholders that specify items that can be present in a position.

Here’s a table that shows how the use of Brackets can affect results:

 

Notes on Brackets

• Brackets can be troublesome. If your query returns null results, your Brackets might be the source of the issue.
• Brackets can contain ranges like [a-f] or [4-8]. Following are all equivalent Bracket statements:

• • [0-2] = [012]
  • [0123456789] = [0-9] = [0-23-9]
  • [a-c] = [abc]
  • [0-3a-d] = [0123abcd]

• Bracketed letters are NOT case sensitive.
• Brackets can only take the place of ONE position:

• • You cannot bracket a number greater than 1 digit.
  • [0-24] does NOT give the numbers zero through twenty-four; instead, it will be interpreted as zero through two or four or as the equivalent Bracket [0124].

• Ranges cannot be backwards or looped. Examples of invalid Brackets: [9–2] and [x–a].
• Querying of certain punctuation will only work within Brackets. Examples follow:

• • [COMMA] to search for commas
  • [_] to search for underscores

Examples that Use Underscore and Bracket

Brackets are commonly used in discharge diagnosis (DD) queries. DD codes frequently begin with a bracketed list of punctuation to ensure you get the start of a DD code and that your queried text won’t appear in the middle. Underscores are also useful in DD queries. Note the use of both the Brackets and Underscores in the following excerpt from the CDC Firearm Injury v1 CCDD Category:

…..(,^[;/ ]W3[23]^,andnot,(,^[;/ ]W3[23]___[DS]^,or,^[;/ ]W3[23].___[DS]^,),),…..

This excerpt uses Underscores in its negations. A “D” or an “S” in the seventh place of this ICD10 code indicates that this is a subsequent or sequalae visit and is not an acute firearm injury ED visit. By using a series of Underscores, the writers could specify what they didn’t want in the seventh place without specifying the middle part of the DD code that came before it.

The ICD10 codes in this example specify that the code must be preceded by a semicolon, forward slash, or space. Note the bracketed [23]: this will capture multiple forms with a single statement. For example, the following ESSENCE free-text codes are equivalent, with the second being more concise:

^;W32^,or,^/W32^,or,^ W32^,or,^;W33^,or,^/W33^,or,^ W33^ = ^[;/ ]W3[23]^

 

We thank Senior Data Analyst Zachary Stein for volunteering to write a series of articles about free-text coding. Stein, formerly with the Kansas Department of Environment and Health, does epidemiologic work to support NSSP efforts. Stein is an active participant in the NSSP CoP. He initially wrote about free-text coding as an entry on the NSSP CoP Syndrome Definition Committee forum. The forum generated considerable interest, inspiring this series. Stein acknowledges input provided by others who contributed to the forum post.

Data Quality Corner

Questions and Tips

Q: What does an API “response” look like?

A: An application programming interface, or API, is a structured and consistent way for one machine (or application) to exchange information with another machine or application. One machine will make an API request (URL with query parameters and display format) and the other will respond. An API response (the information returned to the requestor) using the ESSENCE APIs is either a table of data or a time series image.

The most common API response is a table. In this example, Table Builder provided output in CSV format (JSON is an option, too). Shown below is a table of counts for a particular Chief Complaint (CC) and Discharge Diagnosis (DD) category by quarter, HHS Region, and National Center for Health Statistics age group. Here, we have sent a request to ESSENCE to summarize certain information for us and then return aggregate results. You can automate this query to update over time and then further summarize or visualize it in whatever way suits your needs.

Resources

R for Data Science, R Markdown: The Definitive Guide, Text Mining with R

This explanation was provided by CDC Health Scientist Aaron Kite-Powell from his presentation at the ISDS 2019 Annual Conference: How to Use RStudio with ESSENCE Application Programming Interfaces.

TIP—How to Make an ESSENCE API URL Shorter. Occasionally, the API URL you created for the ESSENCE query will be very, VERY long. For example, you might have reason to explicitly include all facilities, all counties, or all ZIP codes for a site. Consequently, the URL character length might be too long to assign to the “url <-” object. When this occurs, all you have to do is split the URL into two or more strings when creating objects, and then pull them together later.

In the code chunk shown below, we start with one long URL (use your imagination here) and break it into two pieces. Then, to create the object “url3,” we paste them together. Finally, to create the “url,” we clean-up “url3” by removing the carriage return (added when you separated the original URL by pressing Enter on your keyboard) and replace it with nothing. The final URL can be passed to ESSENCE along with your authentication credentials to return your results.

url1 <- "https://essence.syndromicsurveillance.org/nssp_essence/api/very_very_very_long_URL_ver_long_use_your_imagination_here...."
url2 <- "still_going_even_longer_here..........."
url3 <- paste(url1, url2, sep="")
url <- gsub("\n", "", url3)
#Resulting url
print(url)

## [1] "https://essence.syndromicsurveillance.org/nssp_essence/api/very_very_very_long_URL_ver_long_use_your_imagination_here....still_going_even_longer_here..........."

TIP for Writing API Authentication Code. To extract data from ESSENCE, you start by passing authentication information from your RStudio session to ESSENCE so that it knows what data you are allowed to see. Never include your username and password in your code. Instead, let the keyring package do the work. When you run this line of code (with your username entered in the username quotes)

key_set(service = “essence”, username = “username”)

a pop-up will appear in RStudio where you can enter your password. (RStudio will store your password for the remainder of the session without rerunning that line of code.) Once you enter your password in the pop-up, be sure to “comment out” this line of code by adding a hash mark before it, as shown below in this code:

httr::set_config(config(ssl_verifypeer = 0L))
library(jsonlite)
library(keyring)
#key_set(service = "essence", username = "username")

Spotlight on Syndromic Surveillance Practice

Since June 2016, the Centers for Disease Control and Prevention has had a 3-year cooperative agreement with the International Society for Disease Surveillance to facilitate the National Syndromic Surveillance Program’s Community of Practice. Once the Community of Practice had been implemented, the authors examined the value it had added and whether the goals for it had been realized. This Executive Perspective, published in the February issue of Public Health Reports, describes how a community of practice for syndromic surveillance was designed and its effectiveness in building public health capacity.

 

Building State and Local Public Health Capacity in Syndromic Surveillance Through an Online Community of Practice¹

Public health resources (and expertise) vary from jurisdiction to jurisdiction and state to state. The Centers for Disease Control and Prevention (CDC) assists public health departments through funding and technical support. To increase state and local public health capacity in syndromic surveillance, CDC’s National Syndromic Surveillance Program (NSSP) took a two-pronged approach:

• To support the science, CDC NSSP developed technical infrastructure: the BioSense Platform. This cloud-based platform hosts tools and resources for data collection storage, analysis, and exchange.
• To support practice, CDC invested in people. CDC did this by funding a 3-year cooperative agreement to formalize, manage, and facilitate an online community of practice (CoP) for syndromic surveillance¹ with the goal of fostering learning and knowledge sharing. The cooperative agreement to support the CoP was competitively bid and awarded to the International Society for Disease Surveillance (ISDS).

The article describes capacity building as “activities that strengthen and maintain the infrastructure and resources necessary to sustain or improve system, organizational, community, or individual processes and competencies.”² Without the capacity to expertly conduct syndromic surveillance, public health practitioners would be limited in their ability to detect, monitor, and characterize potential public health concerns.

NSSP and ISDS team members searched the literature to identify best practices applicable to CoP design. Through surveys, needs assessments, and discussions with experts in syndromic surveillance, they identified five categories of CoP activities: (1) Community-driven Leadership, (2) Member Participation and Engagement, (3) Collaboration and Networking, (4) Knowledge Sharing and Training, and (5) Problem Solving.

These categories are explained and examples are provided. The category on Community-Driven Leadership, for instance, describes the member-appointed Steering Committee. The Steering Committee is a conduit to the community, identifying knowledge gaps, member needs, and topic areas around which workgroups and subcommittees can be set up to focus on these needs. The Collaboration and Networking category describes how the community rallied to refine the technical specifications for transmitting data (published in the Implementation Guide for Syndromic Surveillance).³ The Knowledge Sharing and Training category cites several examples that demonstrate how public health jurisdictions use syndromic surveillance to improve specific areas of public health.

Further, the article describes the CoP membership, making the point that these analysts, epidemiologists, and other health practitioners who are geographically and professionally isolated have worked together to develop a vibrant community. The CoP’s online collaboration opportunities have stimulated innovation in practice and improved the technical infrastructure for conducting syndromic surveillance. The CoP’s momentum is considerable and it has demonstrated the ability to build capacity.

¹Gould DW, Lamb E, Dearth S, Collier K. Public Health Reports [Internet]. 2019 Feb 14 [cited 2019 May 16];134(3):223–227. Available from: https://doi.org/10.1177/0033354919828713

²Centers for Disease Control and Prevention. Strengthening Public Health Systems and Services through National Partnerships to Improve and Protect the Nation’s Health [Internet]. 2018 [cited 2019 May 16]. Available from: https://www.cdc.gov/publichealthgateway/partnerships/capacity-building-assistance-OT18-1802.html

³ Centers for Disease Control and Prevention. Public Health Information Network guides [Internet]. 2018 Dec 19 [cited 2019 May 28]. Available from: https://www.cdc.gov/phin/resources/phinguides.html.

Program

Technical Updates