1. Capacity for Public Health Informatics among Local Health Departments J. Mac McCullough, PhD, MPH Assistant Professor School for the Science of Health Care Delivery Arizona State University

2. Acknowledgements Co-author: Kate Goodin, MPH Program Manager, Epidemiology & Data Services Maricopa County Department of Public Health 2

3. Public Health Informatics The application of IT and IS to public health practice, research, and learning. An evidence-based way of strengthening the work of a public health department –Enhance capacities to perform surveillance, monitor outbreaks, respond to emergencies, etc. –Can interact with IT from clinical sector to further boost capacity 3

4. Current Use of PH Informatics Informatics SystemProportion of LHDs Currently Using Immunization registry 85.8% Electronic disease registry 75.8% Electronic syndromic surveillance system 66.5% Electronic lab reporting 51.4% Electronic health records (EHRs) 25.1% Health information exchanges (HIEs) 13.9% 4

5. What’s at Stake 5 Proportion of Physicians Using EHRsProportion of LHDs Using EHRs Not all types of PH informatics are rapidly diffusing through the public health system.

6. What’s Known & What’s Not Known Certain LHDs are more likely to use specific informatics functionalities. Not known whether LHDs tend to adopt only specific systems or whether LHDs invest in broader informatics capacities –May be interactions between systems (e.g., use of EHR, participation in HIE) –May be economies of scale—most hospitals have a CIO 6

7. Study Objective Objective: to test for patterns in the presence of public health informatics functionalities within LHDs –Accomplished through the creation of an empirical classification of LHD informatics capacities. –This empirical classification can then be used to explore correlates of informatics capacity. 7

8. Methods Used secondary data from 2013 NACCHO Profile Survey of Local Health Departments –NACCHO data are the single largest source of data on LHDs –Conducted regularly, contain data on LHD structure, finance, services, …., informatics. –Content can change across years Data available on informatics usage from n=505 LHDs from across U.S. 8

9. Creating a Typology Hierarchical cluster analysis used to categorize LHDs according to public health informatics capacity. –Calculated via Ward’s Method. Three-cluster measure was determined to provide optimal combination of data fit and parsimony. LowHighMid 9

10. Predictors of Interest: LHD Characteristics Finances Per capita revenues Clinical revenues State-sources Federal-sources Workforce FTEs per capita Any informatics personnel Services Offered Provision of ~40 different public health services Leadership/Governance Local board of health Freestanding versus part of health and human services agency Single county vs. other jurisdiction Authority to impose fees State vs. local governance 10 After developing typology, use chi 2 and t-tests to explore category composition according to: All data came from 2013 NACCHO Profile

11. 11 Findings: Type of Functionality Percent With FunctionalityDifference Between Groups Total Low (n=112) Mid (n=92) High (n=255) Low vs. Mid Low vs. High Mid vs. High Immunization Registry 85.8%49.1%98.9%97.3%*** Electronic Disease Registry 75.8%18.8%93.7%93.3%***** Electronic Syndromic Surveillance System 66.5%47.3%60.9%76.9%*** Electronic Lab Reporting 51.4%17.9%0.0%84.7%*** Electronic Health Records 25.1%17.9%19.6%30.2%**** Health Information Exchange 13.9%5.4%6.5%20.4%*** * p <.05** p <.01*** p <.001 LHD informatics capacity was clustered into three distinct groups. The LHDs with the lowest level of informatics usage had significantly lower levels of usage for all six functionalities assessed.

12. Characteristics of Low, Mid, High Capacity LHDs High capacity LHDs: Disproportionately serve large populations (> 500,000) Receive significantly higher revenues from Medicare/Medicaid (likely means they engage more in direct services and thus bill CMS) More likely to employ IT personnel Low capacity LHDs: More likely to be multi-county or other complex jurisdiction types Less likely to have an executive director with a clinical background 12

13. Public Health Services Offered vs. Informatics Capacity Low-capacity LHDs provided significantly fewer public health services than LHDs with mid-or high-levels of informatics capacity (p <.01). Differences in service provision: Most pronounced for Population Focused services (e.g., STD screening, tobacco prevention, unintended pregnancy) Least pronounced for Individual Focused services (e.g., behavioral health, HIV tx, obstetrical care, substance abuse) 13

14. Discussion A diverse matrix of factors appear to impact an LHD’s informatics capacity: Setting, finances, governance, leadership, and services offered. High- and low-capacity LHDs differed across all six informatics capacities This consistent pattern across all six systems suggests a deficit of informatics capacities in certain LHDs relative to others. 14

15. Discussion Commonly state-supported applications (e.g., immunization registries) saw higher levels of use among mid-capacity LHDs: LHD therefore operates more akin to information consumers than information brokers. State-level involvement may promote broader informatics capacity among LHDs. Association between service provision and informatics capacity especially prevalent for population-focused public health services May emphasize the role that informatics plays for specific public health services and the symbiotic nature of broad- based capacity for public health informatics and broad- based provision of population-focused services. 15

16. Limitations Cross sectional study: study explored associations and correlates and did not seek to ascribe causality. Partitioned data into training and validation sets. Typology characteristics remained highly consistent across these two iterations. Self-reported data: systematic over- or under-reporting possible, though previous studies found longitudinal consistency in NACCHO informatics data. No measures available for intensity or effectiveness of services provided. 16

17. Conclusion Typology represents a new conceptualization of department-wide informatics capacity. Some LHDs have strong, broad capacity for informatics, others are lagging. How can low-capacity LHDs can maximize the value of informatics to their work and the communities they serve, given their lower levels of service provision relative to high-informatics capacity LHDs? A third group is doing well with common (and commonly state- supported) applications but lags in more advanced system capacity. How can we work to promote adoption of less common technologies? Consideration to state-level factors may be especially important for these LHDs. Future studies might explore the direction and causal nature of the relationship between service provision and informatics capacity. 17

18. Thank you. Questions? Mac McCullough mccullough@asu.edu

19. Public Health Services Examined Individual-focusedPopulation-focused Basic Home health care Basic Chronic disease programs Adult immunizations Oral health Blood lead Maternal and child health Child immunizations Prenatal care Communicable/inf. disease Physical activity EPSDT Primary care HIV/AIDS STDs Family planning School health Nutrition Specialized MCH home visits Well-child clinic Other STDs Chronic disease WIC Specialized Tuberculosis Injury Expanded Behavioral or mental Tuberculosis Injury Cancer HIV/AIDS Tobacco Mental illness Cardiovascular disease Obstetrical care Unintended pregnancy Substance abuse Diabetes School-based clinics Expanded Syndromic surveillance High blood pressure Substance abuse Behavioral risk factors Violence From Bekemeier et al., Classifying local health departments on the basis of the constellation of services they provide. American Journal of Public Health. 2014;104(12):e77-82.

20. Transformational PH Informatics: Surveillance Traditional disease surveillance –Physicians report specific diseases upon diagnosis  Health department follows up on reported cases. –Even with timely and accurate reporting, not a good method of identifying emerging outbreaks. What about unreported cases?? Informatics-based surveillance –E.g., BioSense, automated surveillance system that receives data from hundreds of hospitals nationwide. –Can mine free-text chief complaint fields to identify disease patterns in nearly real-time. Can use a well-functioning disease surveillance system for fundamentally different purposes. 20

21. Maturation of PH Informatics 2005200820102013 Use of IT in the field Wireless access to LPHA IT disaster recovery planning Federal IT standards initiatives Electronic health records (EHRs) Health Information Exchanges (HIEs) Immunization registry National health information network Practice management system Electronic Disease Reporting system Electronic Lab reporting Electronic syndromic surveillance system

22. Creating a Typology Cluster Analysis: assembling observations into groups based on their similarity/dissimilarity on selected measures. 22

23. Creating a Typology 23 Hierarchical cluster analysis: method that generates a tree-like structure based on distance/ similarity between observations Example: NFL Teams, clustered through 2015 season statistics 1) Good offensive teams 2) Good overall teams 3) Mediocre teams 4) Good defenses 5) Bad teams 6) Inconsistent teams