1. Kansas Data-Driven Prevention Initiative Data Strategy and Activity
Fan Xiong, MPH Senior Epidemiologist
Kansas Board of Pharmacy
Kansas Data-Driven Prevention Initiative Program
Kansas Department of Health and Environment, Bureau of Health Promotion

2. Background
The Kansas Data-Driven Prevention Initiative Program (DDPI)

3. General Consensus of What Happened
Source: Working Kansas Opioid State Plan. Draft.

4. Data-Driven Prevention Initiative Program
The Kansas Department of Health and Environment (KDHE) is funded for this cooperative agreement with the CDC from August 2016 to July 2019.

5. Long-Term Outcomes Decreased rates of opioid abuse
Increased opioid use disorder treatment (ultimately want decrease)
Decreased rate of ED visits related to opioids
Decreased drug overdose death rate, including both opioid and heroin death rates
Improved health outcomes in high-burden regions (e.g., “hot-spots”).

6. Strategies & Selected Activities Intermediate Outcomes
Data Strategy 1: Enhance surveillance of prescription drug and heroin abuse and overdose
Strategies & Selected Activities
Short-term Outcomes
(1-2 years)
Strategy: Enhance surveillance of prescription drug and heroin abuse and overdose
Activity: Enhance emergency department (ED) syndromic surveillance data for drug overdose related medicall encounters.
Activity: Develop BRFSS module to assess the prevalence of developing opioid-related use disorders.
▪ More timely receipt of key data sources*
▪ Increased use by KDHE and partners of standard reports for surveillance*
Intermediate Outcomes
(2-3 years)
Outputs
Syndrome definitions for drug overdoses;
Proposed BRFSS questions related to risk of developing opioid use disorders;
Documentation of data access;
Reports and factsheets;
Identify necessary data sources;
▪ Enhanced infrastructure among partners identified in strategic planning process*
▪ Increased capacity and ability of KDHE and partners to access, analyze and apply data to PDO and drug abuse interventions*

7. Strategies & Selected Activities Intermediate Outcomes
Data Strategy 2 : Enhance public health access and application of PDMP data
Strategies & Selected Activities
Short-term Outcomes
(1-2 years)
Strategy: Enhance public health access and application of PDMP data
Activity: Develop data use agreement with the Kansas Board of Pharmacy.
Activity: Disseminate statistical report and data to public health agencies.
▪ More timely receipt of key data sources*
▪ Increased use by KDHE and partners of standard reports for surveillance*
Intermediate Outcomes
(2-3 years)
Outputs
Public health has access to PDMP data for public health surveillance and evaluation.
Develop key metrics for public health surveillance and evaluation.
Develop data reports and extracts for public health agencies.
▪ Increased capacity and ability of KDHE and partners to access, analyze and apply data to PDO and drug abuse interventions*

8. KTRACS Data Dashboard
County-Level Maps, Charts, and Data Extracts for Key Prescription Drug Indicators
Prescription Drug Indicators were defined based on inputs from local health department epidemiologists’ need for data and a review of other states and jurisdictions use of prescription drug monitoring program data for public health surveillance and evaluation.

9. Select a drug to show only the data for that drug.
Uses a Tableau Story to distribute different level of prescription drug indicators (e.g., prescription-level and patient-level) and for different types of visualization (e.g., charts, maps).
Select a drug to show only the data for that drug.
Use Tableau’s Download Button to Extract the Data by Age-groups and Drug.
Version 2.0 is available at:

10. Rates are not age- adjusted.
Rates are computed as either prescriptions per 100 residents or patients per 100 residents.
Select a drug to show only the data for that drug.
Use Tableau’s Download Button to Extract the Data by Age-groups and Drug.
Version 2.0 is available at:

11. Rates are not age- adjusted.
Rates are computed as either prescriptions per 100 residents or patients per 100 residents.
Select an indicator to show maps or data for that indicator.
Use Tableau’s Download Button to Extract the Data by Age-groups and Drug.
Version 2.0 is available at:

12. Future: June-August 2018
Develop “county-profile” or “region-level” reports for data dashboard.
Include January 1 – June 30, 2018 KTRACS Data and data from 2014 – 2017.
Remove patient-level indicators (difficult to interpret) and keep prescription-level indicators (more timely).
Define drugs by AHFS Classification for a wider inclusion of opioids, benzos, stimulants, and muscle relaxant drugs.
Include morbidity and mortality surveillance data, such as emergency department admissions, hospital discharge, poison control center, Kansas BRFSS, U.S. Census Bureau data, and American Community Survey Data.
Explore other data sources, such as drug seizures, toxicology results, EMS data, etc.
Explore non-traditional public health data sources, such as Google Trends.

13. The percentage of opioid patients with any overlapping opioid and benzodiazepine prescriptions is higher for Kansas female patients – due to a higher rate of benzodiazepine prescriptions.

14. Behavioral Risk Factor Surveillance Survey
What is the prevalence of prescription pain medication misuse?
Misuse is defined as using a prescription pain medication without a prescription or in higher quantities than prescribed by a doctor.

15. Kansans used opioid prescriptions not prescribed to them or higher and more frequently than prescribed.
This population is at higher risk of a heroin-related or illicit opioid overdose given their past history of misusing prescription opioids.

16. Most said pain was the reason for misuse, but some Kansans cited a reason other than pain, such as the feeling it caused.

17. Local Kansas BRFSS Data, 2011 – 2015 http://www. kdheks
County-level and Public Health Preparedness Region Level
Example Indicators Related to Increase Risk of Substance Use Disorders:
Percentage of Adults Who Are Binge Drinkers
Percentage of Adults Who Reported Heavy Alcohol Consumption in the Past 30 Days
Percentage of Adults Who Were EVER Diagnosed with a Depressive Disorder
Percentage of Adults Who Reported Their Mental Health Was Not Good on 14 or More Days in the Past 30 Days
Percentage of Adults Who Reported Their Physical Health Was Not Good on 14 or More Days in the Past 30 Days
Percentage of Adults Who Reported Their Poor Physical or Mental Health Kept Them From Doing Their Usual Activities

18. Syndromic Surveillance Emergency Department
Access to the Kansas Syndromic Surveillance Emergency Department Admissions data is through the Bureau of Epidemiology and Public Health Informatics at the Kansas Department of Health and Environment. Contact or for more information.

19. Kansas Syndromic Surveillance Work
Opioid-related Poisoning Syndromes
Non-cancer chronic pain-related syndromes
Purpose: Detect emerging trends in potential opioid-related poisoning incidence.
CDC syndromes were developed in partnership with multiple jurisdictions across the United States as a part of the enhanced opioid surveillance work.
Must be customized for specific jurisdictions due to variations in data quality.
CDC-Opioid v1 Syndrome
CDC-Heroin v3 Syndrome
Purpose: Detect emerging spatiotemporal trends in potential opioid-prescription seeking behavior in Kansas.
Mapped ICD-9-CM Diagnosis codes to ICD-10- CM diagnosis codes based on CDC definition of chronic pain related diagnosis codes.
Used 2018 ICD-9-CM to ICD-10-CM General Equivalence Mappings (GEMS) file from CMS.
Includes chronic pain and back pain- musculoskeletal-related pain conditions, and pain disorders with psychological factors
Excludes any mention of a neoplasm diagnosis code or other neoplasm-related pain diagnosis, sickle cell diseases.

20. CDC – Opioid v1 Syndrome Cases Descriptive Statistics
Data Source: January 1, 2016 – April 20, Emergency Department Admissions reported to the National Syndromic Surveillance Program. Based on data extracted on April 20, 2018.
Birthyear and Original Sex Demographics
Female: 55.5% (N=1,156) Male: 44.5% (N=928)
Epidemic Cohorts:
Cohort: 30.0% (N=626) Cohort: 26.0% (N=542) Epidemic cohorts were defined using: Huang X, Keyes KM, Li G. Increasing Prescription Opioid and Heroin Overdose Mortality in the United States, 1999–2014: An Age–Period–Cohort Analysis. American journal of public health Jan;108(1):131-6.

21. Kansas – Non-cancer Pain Syndrome
Data Source: January 1, 2016 – April 20, Emergency Department Admissions reported to the National Syndromic Surveillance Program. Based on data extracted on April 20, 2018.
Birthyear and Original Sex Demographics*
Female: 60.1% (N=15,956) Male: 39.9% (N=10,597)
Epidemic Cohorts*:
Cohort: 26.7% (N=7,084) Cohort: 25.0% (N=6,650) Epidemic cohorts were defined using: Huang X, Keyes KM, Li G. Increasing Prescription Opioid and Heroin Overdose Mortality in the United States, 1999–2014: An Age–Period–Cohort Analysis. American journal of public health Jan;108(1):131-6.

22. KTRACS – Patients with Opioid Prescriptions, KTRACS 2016-2017, N=970,319
Data Source: Prescriptions filled from January 1, – December 31, 2017 KTRACS Deidentified Dataset from the Kansas Board of Pharmacy.
Birthyear and Original Sex Demographics
Female: 34.1% (N=330,721, Opioid Rx= 2,105,939) Male: 25.8% (N=250,714, Opioid Rx= 1,460,210) Missing: 40.1% (N=388,884, Opioid Rx= 1,318,842)
Epidemic Cohorts:
Cohort: 28.1% (N=272,798) Cohort: 20.1% (N=194,240) Epidemic cohorts were defined using: Huang X, Keyes KM, Li G. Increasing Prescription Opioid and Heroin Overdose Mortality in the United States, 1999–2014: An Age–Period–Cohort Analysis. American journal of public health Jan;108(1):131-6.

23. Google Trends
For an example of connecting Google Trend keywords to disease or condition prevalence:

24. Image borrowed from webinar presentation by Christina Zurla, ICF and Ashley Wiers, Google
Searching for “Opioids” are correlated with the increasing incidence of opioid-related deaths – shows interest over time.

25. Who are Searching for Opioids?
Image borrowed from webinar presentation by Christina Zurla, ICF and Ashley Wiers, Google

26. Why are they Searching for Opioids?
Image borrowed from webinar presentation by Christina Zurla, ICF and Ashley Wiers, Google

28. Recommendations for Public Health Surveillance
Insights from Epidemiological Analysis of Key Surveillance Data

29. Birth Cohorts Year of Death
Figure 1. Percent of All Deaths Due to Drug Poisonings by Year of Death, Gender, and Birth Cohort, Kansas residents with a birth year from 1940 to 1989, Kansas Vital Statistics Data
Birth Cohorts
More than 10% of all deaths are due to drug poisonings for Kansas birth cohorts after 1970.
Year of Death

31. Emerging drug poisonings high-burden regions are counties and locations with a Median Age between 21.9 to 42.0.

32. Contact Fan Xiong, MPH Senior Epidemiologist
Kansas Data-Driven Prevention Intiative Program
Kansas Board of Pharmacy
Kansas Department of Health and Environment
Topeka, Kansas