# Presentation: 347922

## BioSense Data Analyses and Anomaly Characterization

- Gabriel Rainisch, MPH
- Colleen A. Martin, MSPH
- Division of Emergency Preparedness and Response
- National Center for Public Health Informatics
- Centers for Disease Control and Prevention
*"The findings and conclusions in this presentation are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention."*

## Purpose & Outline

- Purpose: Summary of recent analyses using BioSense data
- To inform alerting & monitoring protocols
- Improving the utility of BioSense
- Outline:
- BioSense overview
- Descriptive analyses
- Cluster detection methods and anomaly characterization
- Epidemiologic studies

## BioSense Data Sources

- Department of Defense (DoD; n=466) and Veterans Affairs (VA; n=905) outpatient medical facilities
- Daily data
- ICD-9 diagnosis codes and CPT procedure codes
- Hospital real-time data (n=34)
- Chief complaints, diagnoses, demographics
- Patient class: emergency department, inpatient, outpatient

**Notes:**
Since 2003, we have received the following data:

Foreshadow:  “In a few moments I will describe to you about analyses comparing these data and patient classes”

Hospital data: is much richer and complicated – a lot of the analyses presented here is focused on understanding the intricacies of this data source so that we can improve it’s utility for biosurveillance.

## Disease Indicators: Syndromes (N = 11)

- Botulism-like
- Fever
- Gastrointestinal
- Hemorrhagic illness
- Localized cutaneous
- 	lesion
- Lymphadenitis
- Neurological
- Rash
- Respiratory
- Severe illness/death
- Specific infection
- (_http://www.bt.cdc.gov/surveillance/syndromedef/index.asp_)

-  Monitor critical bioterrorism associated and natural infectious disease outbreaks

## Disease Indicators: Sub-syndromes (N = 78)

- Newly developed
- Monitor infectious, chronic, and other disease indicators
- More granular than the 11 syndromes
- Require ongoing evaluation and enhancement
- 46 sub-syndromes map to syndromes
- Syndrome: Gastrointestinal
- Sub-syndromes included: diarrhea, abdominal pain, gastrointestinal hemorrhage
- 32 sub-syndromes do not map to syndromes
- Examples: Allergy, Injury, Excessive heat

**Notes:**
Coincidentally, we were looking at these 3 during the E-coli outbreak

## Analyses

## Descriptive Analyses

- Understand and compare syndrome data from long-term sources (VA/DoD) vs. newer (Hospital Real-Time)
- Explore distribution of new disease indicators (sub-syndromes)
- Understand the capabilities for disease monitoring using both existing and new data and indicators

**Notes:**
Purpose: Increase data knowledge, inform data analysis

## Visits Assigned to a Syndrome, Feb-June 2006(in thousands)

- Hospital facilities on average receive 7 times the mean monthly visit volume of VA/DoD facilities
- The percent of total visits meeting a syndrome definition is similar for all data sources

| Source | Mean Visits Per    Facility-Month | Binned Visits | % Binned |
| --- | --- | --- | --- |
|   DoD
(n=466) | 230 | 1,927 | 15.7 |
|    VA
(n=863) | 190 | 2,059 | 9.6 |
| Hospital
  (n=30) | 1,708 | 212 | 11.6 |

**Notes:**
Hospital facilities on average receive 7 times the mean monthly visit volume o VA and DoD facilities – likely because Hospital receives ED & inpatient data as well compared to VA/DoD

The percent of total visits meeting syndrome definitions is similar for all data sources

##  Percent of Visits Assigned to a Syndrome

- Respiratory and Gastrointestinal combined, comprise 60-75% of binned visits
- The only marked difference between data sources is for the Gastrointestinal syndrome (Hospital, 30%; DoD 18%; VA, 11%)

## Percent of Visits Assigned to a SyndromeHospital Real-Time Data

**Notes:**
There is a certain amount of variability in the diseases assigned to syndromes by patient class:
-  Less severe illnesses: picked up in outpatient data (like lymphadenitis)
-  More severe illnesses: picked up in Inpatient data (like Severe Illness/Death)

## Hospital Real-Time Final Diagnoses:20 Most Common Sub-Syndromes (Feb-Aug 2006)

**Notes:**
Many in the top 20 are unrelated to infectious diseases, and are chronic in nature.

With so many data types, there are numerous ways to analyze the data.  This graph shows all patient classes.  Also only shows final diagnoses, not chief complaints.  Each possible graph 

## Sub-syndrome Demographics

- Age effect:
- Older ages: more chronic conditions
- Younger ages: more infectious, acute conditions
- Variation among sub-syndrome distributions by hospital system:
- Differences in patient population (e.g. ages)
- Variation in patient class mix
- outpatient clinics, emergency department, inpatient

**Notes:**
Hosptial systems catering to specific age groups: Children’s & Adult Hospitals

## Descriptive Analyses

- Newer Hospital real-time data
- Expands capabilities for disease monitoring
- Is similar to VA/DoD data in many respects
- Combined analyses appear to be feasible

## Cluster Detection & Anomaly Characterization

- Modified CuSum C2 Statistic: “W2”
- 7-day average with 2-day lag
- Weekdays compared with weekdays & weekend days with weekend days
- Calculate a recurrence interval:
- Reciprocal of a p-value
- Recurrence interval (RI) = # of days between two observed similar residual values
- Better analytic method that produces anomalies at an appropriate frequency

**Notes:**
Calculating the RI: We’ve been doing this empirically which allows us better control over the # of anomalies, which, in turn, contributes to a better protocol for monitoring BioSense data

## Anomaly Characterization

- What characteristics identify anomalies most likely to be of potential importance for further evaluation?

## Ranking Data Anomalies

- Metrics already in use:
- Recurrence Interval (RI)
- Observed vs. previous max count
- Observed / expected (relative risk)
- Observed – expected (residual)
- # of consecutive days flagged
- Other anomalies at the same facility/area
- Metrics slated for evaluation:
- Severity of illness (e.g. pneumonia vs. cough)
- Homogeneity of chief complaints/diagnoses, age, gender, etc.

**Notes:**
These are some of the metrics we use

Severity of illness is subjective:  will pursue research to make objective

Metrics utility is specific to monitoring purpose:
Example:  using Observed/Previous Max to monitor the respiratory syndrome during the summer may not  be effective for catching flu related anomalies, however it is better for a syndrome with less seasonality.

## BioSense Epidemiologic Studies

- Influenza
- Neurological Syndrome
- Respiratory Syncytial Virus (RSV)
- Injuries following Hurricane Katrina
- Heat Related Illnesses

## Heat Related Illness

- Goal:  Explore utility of BioSense data for heat related illness surveillance
- Hospital Real-Time Data (n= 34 hospitals)
- Study period: June 1st – August 10th 2006
- Sub-syndrome: “Heat, Excessive”
- Diagnosis: 1 ICD-9 Code: E900.0 (Excessive heat due to weather conditions)
- Chief complaint suggestive of heat injury

**Notes:**
HRI cases can be found using: diagnosis codes or chief complaints

E900 is an E-code.  The “E” in E-codes stands for External Causes of Injury and Poisoning.
Chief complaints are text based

## Heat Related Illness Visits, BioSense Application, ICD-9 Diagnosis (n=139 Visits)

**Notes:**
Screenshot from BioSense app.
Showing Count and Rate
Stars=statistical elevation in the count

## Heat-Related Illness (HRI) Diagnosis, June 1- August 10, 2006

- Several HRI diagnoses were identified that are not being used in BioSense
- Visit counts
- All HRI diagnoses: 217 visits
- HRI diagnoses identified by current BioSense methods: 139 visits
- Additional HRI diagnoses should be included in BioSense

**Notes:**
Preliminary Data

## Heat-Related Illness (HRI) Chief Complaints, June 1- August 10, 2006

- 217 visits with HRI diagnoses
- 194 visits with HRI diagnosis but not an HRI chief complaint:
- 125 of these visits had chief complaints. Such as:
- Malaise and fatigue:  24
- Syncope and collapse:  19
- Chest pain:  19
- Dizziness:  17
- Dehydration:  14

**Notes:**
Preliminary Data 

## HRI Indicators

- Breakdown of 297 HRI visits:
- Male: Female   66%:34%
- Age:
- 0-3 yrs.	2%
- 4-11 yrs.	4%
- 12-19 yrs.	12%
- 20-49 yrs.	45%
- 50+ yrs.	37%
- 19% of patients with an HRI chief complaint also mapped to the “Cerebrovascular Disease” sub-syndrome

**Notes:**
Preliminary data
Application supports association of HRI with chronic co-morbidities and being older

## HRI Analyses

- Text parsing creates challenges:
- “HEAT MURMUR”;
- “IRREGULAR HEAT BEAT”;
- “rapid heat beat”;
- “HEAT PROBLEMS”; ???
- Next steps:
- Correlate with temperature data
- Examine distribution of chronic conditions among historical visits

## Summary

- Descriptive analyses
- Hospital Real-Time data adds new capabilities
- Disease indicator patterns similar to VA/DoD data
- Cluster detection methods: modified methods and evaluation of ranking criteria can help prioritize anomalies for further investigation
- Epidemiologic studies: ongoing to correct problems, expand capacities, and improve utility of BioSense

## Acknowledgements

- Jerry Tokars, MD, MPH
- Colleen Martin, MSPH
- Data Quality & Programming Team:
- Roseanne English
- Paul McMurray, MDS
- Felicita David, MS
- Laura Hall, MPH

## For more information

- Gabriel Rainisch: GRainisch@cdc.gov
- Colleen Martin: CMartin5@cdc.gov@cdc.gov
- BioSense Website: _[http://www.cdc.gov/biosense](http://www.cdc.gov/biosense)_
- Technical Help Desk: 1-800-532-9929
- Secure Data Network question
- BioSense technical support
- BioSense E-mail Address: _[BioSenseHelp@cdc.gov](mailto:BioSenseHelp@cdc.gov)_
- General questions
- Additional user requests
- Problem reporting
- Suggestion for enhancements
- All feedback!