Spatial Analysis of Health Care Utilization in Medicare Beneficiaries with Pneumoconiosis

1. Spatial Analysis of Health Care Utilization among Medicare Beneficiaries with Coal Workers’ Pneumoconiosis and Other Related Pneumoconiosis Ahmed Arif, PhDAssociate ProfessorDepartment of Public Health Sciences Claudio Owusu, MADoctoral Student Department of Geography and Center for Applied Geographic Information Science November 12, 2018

2. Coal • More than 7269 million tons (Mt) are produced worldwide • Top three coal producers • China – 3443 Mt • India – 708 Mt • U.S. – 672 Mt • In the U.S. 30% of electricity is generated by coal

4. Lung diseases among coal miners • Chronic bronchitis • Emphysema • Pneumoconiosis • Asbestosis • Silicosis • Coal workers’ pneumoconiosis (CWP)

5. What is Coal workers’ pneumoconiosis (CWP)? • CWP is an occupational lung disease caused by overexposure to respirable coal mine dust • Inhaled coal dust is deposited in the lung parenchyma leading to the formation of black nodules, inflammation and fibrosis

6. CWP PMF Normal Simple Source: https://emedicine.medscape.com/article/297887-overview

7. Prevalence of CWP in the U.S. • 11.2% in 1970–1974 to 2.1% (2005-2015); Eastern region – 3.3%-3.9% • Prevalence of PMF  severe form of CWP  is rising, especially in the central Appalachian region (KY, VA, and WV)

8. Purpose of the study • To assess the geographical distribution of health care utilization patterns among Medicare beneficiaries with CWP and other related pneumoconiosis. • To conduct spatial analysis of health care utilization among Medicare beneficiaries with CWP and other related pneumoconiosis as they relate to the location of black lung clinics.

9. Medicare Limited Dataset (LDS) administrative claims data • Medicare beneficiaries represent 16% of the total U.S. population or approximately 51 million individuals covered under Part A (hospital) and B (outpatient services). • The LDS includes a set random sample of 5% of the Medicare population

10. Inclusion Criteria • Diagnosis of ICD-9-CM 500.xx-505.xx • Study period of January 1, 2011 through December 31, 2014. • The date of first diagnosis of CWP served as the patient’s index date. • If the patient did not have a diagnosis of CWP then the date of first diagnosis of 501-505 served as the index date.

11. Health Care Utilization • The total counts for the utilization for patients with ICD-500 or those with ICD-501 – 505 were calculated at the county-level by summing • office visits (a) • emergency room visits, (b) and • Hospitalizations (c). • Denominator: Population 18 years and over that have health insurance coverage using the American Community Survey, 5-year estimates 2010-2014. • To obtain an annual rate of utilization, the final results were divided by the number of years (t=4).

12. Mapping • County-level counts and annual rates of utilization for Medicare beneficiaries with CWP and other related pneumoconiosis were mapped. • Cluster-outlier analysis to determine counties with significantly high clustering of health care utilization for CWP and other related pneumoconiosis. • ArcGIS 10.5

13. Results • 86.6% were male • 89.7% ≥ 65 • 89.6% were white

14. Results

16. Black Lung Clinic Program Locations in the U.S. (N=55)

17. Active Mine Locations in the U.S. (N=710) https://tabsoft.co/2JyzE4o United States Energy Information Administration, (2016). Coal Mines, Surface and Underground Layer. Retrieved from: https://www.eia.gov/maps/layer_info-m.php

18. 55 Black Lung Clinics program https://tabsoft.co/2OkuFoM

19. Counts of Beneficiaries for CWP (ICD-9 CM 500), 2011-2014, Contiguous United States

20. Annual Rates of Health Care Utilization for CWP (ICD-9 CM 500), 2011-2014, Relative to BL Clinic Locations, Contiguous United States

21. Counts of Beneficiaries and Annual Rates of Health Care Utilization for CWP (ICD-9 CM 500), 2011-2014, Relative to BL Clinic Locations, Contiguous United States https://tabsoft.co/2Q4Vv5X

22. Cluster-outlier Analysis of Annual Rate of Health Care Utilization for CWP (ICD-9 CM 500), 2011-2014 https://tabsoft.co/2zs5fjZ

23. Counts of Beneficiaries with Other Related Pneumoconiosis (ICD-9 CM 501–505), 2011-2014, Contiguous United States

24. Annual Rates of Health Care Utilization for Other Related Pneumoconiosis (ICD-9 CM 501–505), 2011-2014, Relative to BL Clinic Locations, Contiguous United States

25. Counts of Beneficiaries and Annual Rates of Health Care Utilization for Other Related Pneumoconiosis (ICD-9 CM 501–505), 2011-2014, Relative to BL Clinic Locations, Contiguous United States https://tabsoft.co/2qjCP7g

26. Cluster-outlier Analysis of Annual Rate of Health Care Utilization for Other Related Pneumoconiosis (ICD-9 CM 501–505), 2011-2014

27. Cluster-outlier Analysis of Annual Rate of Health Care Utilization for Other Related Pneumoconiosis (ICD-9 CM 501–505), 2011-2014 https://tabsoft.co/2Oim0DA

28. Conclusions • The spatial analysis shows that rates of health care utilization for CWP are higher in counties with a high number of active mines, particularly in the Appalachian region. • Cluster analysis revealed some challenges in access to health care for individuals with CWP, particularly in some counties in Illinois and Kentucky. • The significance of clusters of health care utilization rates among beneficiaries with other related pneumoconiosis is unknown. • Since CWP and lung diseases that are part of other related pneumoconiosis can coexist, there is a need for further studies to understand the characteristics of these beneficiaries and underlying disease etiology.

29. Acknowledgement • Dr. Christopher Blanchette • Ripsi Patel • Dr. Joshua Noone • Dr. Tyrone Borders The Rural & Underserved Health Research Center is supported by the Federal Office of Rural Health Policy (FORHP), Health Resources and Services Administration (HRSA), U.S. Department of Health and Human Services (HHS) under cooperative agreement # U1CRH30041. The information, conclusions and opinions expressed in this presentation are those of the authors and no endorsement by FORHP, HRSA, HHS, or the University of Kentucky is intended or should be inferred.

30. References • U.S. Bureau of Labor Statistics. Occupational Employment Statistics, May 2017. National Industry-Specific Occupational Employment and Wage Estimates, NAICS 212100 - Coal Mining. Washington, DC; 2018. Retrieved from https://www.bls.gov/oes/current/naics4_212100.htm • United States Energy Information System. Which states produce the most coal? Washington, DC; 2017. Retrieved from https://www.eia.gov/tools/faqs/faq.php?id=69&t=2 • U.S. Census Bureau. Health Insurance Coverage, 2010-2014. American Community Survey (Summary File: B27001). Suitland, MD; 2014. • Anselin L. Local Indicators of Spatial Association—LISA. Geogr Anal. 1995;27(2):93-115. • Mitchell A. The ESRI Guide to GIS Analysis. Volume 2: Spatial Measurements & Statistics. Redlands, CA: Esri Press; 2005. • United States Energy Information System. Coal Mines, Surface and Underground Layer. Washington, DC; 2016. Retrieved from https://www.eia.gov/maps/layer_info-m.php. • United States Office of Surface Mining Reclamation and Enforcement. Abandoned Mine Land Inventory System. Washington, DC: Office of the Interior; 2017. Retrieved from https://www.osmre.gov/programs/AMLIS.shtm • Blackley DJ, Halldin CN, Laney AS. Continued Increase in Prevalence of Coal Workers' Pneumoconiosis in the United States, 1970-2017. Am J Public Health. 2018;108(9):1220-1222. • Blackley DJ, Reynolds LE, Short C, et al. Progressive Massive Fibrosis in Coal Miners From 3 Clinics in Virginia. JAMA. 2018;319(5):500-501.

31. Questions?