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Estimating Mortality from Pneumoconiosis Using Multilevel Spatial Binary Regression Estimating Mortality from Pneumoconiosis Using Multilevel Spatial Binary Regression

Estimating Mortality from Pneumoconiosis Using Multilevel Spatial Binary Regression - PowerPoint Presentation

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Estimating Mortality from Pneumoconiosis Using Multilevel Spatial Binary Regression - PPT Presentation

Rajib Paul PhD University of North Carolina at Charlotte November 6 2019 Joint work with Ahmed Arif Eric Delmelle Claudio Owusu Gabriela Brissette and Oluwaseun Adeyemi ID: 784760

spatial cwp pneumoconiosis level cwp spatial level pneumoconiosis mortality increased county data coal rural death health odds counts rates

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Estimating Mortality from Pneumoconiosis Using Multilevel Spatial Binary Regression

Rajib Paul, PhDUniversity of North Carolina at CharlotteNovember 6, 2019Joint work with Ahmed Arif, Eric Delmelle, Claudio Owusu, Gabriela Brissette, and Oluwaseun Adeyemi

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Slide2

Presenter DisclosureRajib Paul

No relationships to disclose2

Slide3

Research SupportThis project was 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 document are those of the authors and no endorsement by FORHP, HRSA, HHS, or the University of Kentucky is intended or should be inferred.   ©2019, Rural & Underserved Health Research Center, University of Kentucky.

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OutlineMotivationResearch Questions

Methods:DatasetDescriptive AnalysisInferential Statistical Analysis – Regression based approach with spatial covariance modelingResultsMajor FindingsStrengths and LimitationsFinal Remarks4

Slide5

MotivationThe United States generates 30% of its electricity needs from coalCoal

mines are mostly located in rural counties and employ local workforceThe prevalence of Coal Workers Pneumoconiosis (CWP) is 9% (Blackley et al., 2016) among those who worked 25 years or more in coal minesIncreased prevalence is observed in central Appalachia (Kentucky, Virginia, West Virginia)5

Slide6

PneumoconiosisPneumoconiosis represents a group of respiratory pathologic diagnosis due to inhalation and deposition of substances that damage the lung parenchyma It is a syndrome complex manifesting initially as anthracnosis, an asymptomatic discoloration, and occlusion of the lung which progresses to chronic bronchitis, ultimately culminating into progressive massive fibrosis

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CWP and other related PneumoconiosisICD-9 500: CWP, Black Lung DiseaseICD-9 501: Asbestosis

ICD-9 502: SilicosisICD-9 503: Pneumoconiosis due to other inorganic dustICD-9 504: Pneumonopathy due to inhalation of other dustICD-9 505: Pneumoconiosis, unspecified7

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Research QuestionsAggregate Level: Estimate the prevalence of county-level CWP and identify hotspots

Individual Level: Estimate the risk of mortality from Pneumoconiosis and identify factors that are indicative of increased risk for mortalityMultilevel: How individual level and aggregate level risk factors contribute to increased risk of mortality from CWP8

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DatasetTime Period: 2011 – 2014Study Region: IL, IN, KY, OH, PA, VA, and WV

Medicare Limited Dataset (LDS): Administrative claims data that includes a random sample of 5% of the Medicare Fee For Service (FFS) populationDemographic characteristics including county of residence Inpatient claims file that contains ICD-9 diagnosis codes, dates of service, and hospital claims data Carrier file that contains claims data from non-institutional providers such as physicians, physician assistants, and nurse practitioners9

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Study Region: Observed Locations10

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Descriptive Analysis Our descriptive statistics include tables on counts, proportions, quartiles, mean, median, and standard deviationWe include maps of observed counts of county-wise black lung disease and crude prevalence rates

We also map county-wise mortality counts from CWP 11

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Method: Aggregate Level Negative binomial regression on county-wise counts of CWPExplanatory Variables: % Population living below poverty level, Areal proportion within 10miles of mines, % Males, % Whites, % Rural, and No. of underground mines

We include the expected counts as offset variable in our regressionMoran’s I test was used for assessing spatial autocorrelation12

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Offset Variable: Expected CountsThe expected counts were calculated from data for jth county using:

Model Outcome: Standardized Mortality Ratio (SMR) : observed rate/expected rate

 

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Multilevel Logistic RegressionConsidering death as a binary outcome a multilevel logistic regression with county-level spatial random effects was used

Individual level explanatory variables: age, gender, race, disease type (CWP or not), and number of ER visitsCounty level explanatory variables: rural rates and poverty ratesData extraction was done using SAS 9.4 and analyses were done in RStudio/R version 3.6.114

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Spatial Random Effects TermCounty level random effects term requires modeling spatial autocorrelationWe used nonparametric spatial covariance modeling approach based on the latitudes and longitudes of county centroids

A bivariate basis function of the following form is used for spatial smoothing: ||xk – x||2log ||xk – x||Where xk are selected knot locations over the region that control spatial smoothness15

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Spatial Smoothness: Knot Selection16

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Descriptive Statistics17

ER Use among CWP

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Active Coal Mines and Rural Rates18

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CWP: Prevalence Rates and SMR19

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Aggregate Level CWP: Rates20

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Individual Level: Mortality (Binary)21

OR2.50%

97.50%

OR (spatial)

2.5% (spatial)

97.5% (spatial)

(Intercept)

0.001667

0.000684

0.003965

8.00E-04

0

0.0155

Age

1.069471

1.059155

1.080109

1.0703

1.0595

1.0813

gender Male

1.126026

0.829468

1.544001

1.1221

0.815

1.5448

race White

1.245953

0.856595

1.848704

1.2283

0.8295

1.8188

BLD 500

1.317002

1.094909

1.584189

1.2843

1.0486

1.5729

Deviance

2936.8

2711.034

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Multilevel: Mortality with ER22

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Results: InterpretationsIt seems that under adjusted regression, those who have CWP, there is an increased odds of death among those who come to ER (about 2.3%)

It seems that under adjusted regression, those who do not have CWP, there is an increased odds of death among those who come to ER (about 4.8%, almost twice) Among CWP, those who never went to ER had 45% increased odds of death when adjusted for other variablesAmong CWP, those who went to ER once, there odds decreases by 2.4% compared to those who never went to ER23

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Major FindingsThere are 411 (15.8%) cases below age 65, out of which 274 (66.7%) were diagnosed with CWP. Among them 41 (14.9%) died before there 65th birthday

There is an increased amount of ER use among CWPSpatial Cluster: Medicare beneficiaries living in counties in the central Appalachian region had relatively higher rates of health care utilization for CWP compared to other parts of the country24

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LimitationsThis study is ecological, causal effect cannot be establishedOther comorbid conditions have not been considered in the current study

Analysis is based on claims data, other health conditions from electronic medical records are unavailableEmployment status and tenure of employment have strong influence on development of CWP and its severity, that information is unavailable 25

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StrengthsHelps to identify regions with high utilization of Medicare services for PneumoconiosisIdentify hotspots where rates of CWP is higher so that targeted interventions can be planned

Identify which type of Pneumoconiosis has increased risk of mortalityIdentify patterns of ER use and MortalityAn example of secondary data analysis that fused claims data with American Community Survey (ACS) 26

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Final RemarksProvides insights on rural occupational healthIdentifies regions needed attention by HRSA for future planning of black lungs clinic

Provides an assessment of Medicare utilization due to different PneumoconiosisEstimates death risks from CWP and counties with higher poverty rates have increased odds of death from Pneumoconiosis (about 2% increase in odds for 1% increase in poverty rate)27

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Thank You

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ReferencesWade WA, Petsonk EL, Young B, et al. Severe occupational pneumoconiosis among West Virginian coal miners: one hundred thirty-eight cases of progressive massive fibrosis compensated between 2000 and 2009. Chest. 2011;139(6):1458-1462.

Blackley DJ, Halldin CN, Wang ML, et al. Small mine size is associated with lung function abnormality and pneumoconiosis among underground coal miners in Kentucky, Virginia and West Virginia. Occupational and Environmental Medicine. 2014;71(10):690.Dos S Antao VC, Petsonk EL, Sokolow LZ, et al. Rapidly progressive coal workers’ pneumoconiosis in the United States: geographic clustering and other factors. Occupational and Environmental Medicine. 2005;62(10):670.Center for Disease Control and Prevention. Pneumoconiosis Prevalence Among Working Coal Miners Examined in Federal Chest Radiograph Surveillance Programs — United States, 1996–2002. Morbidity and Mortality Weekly Report. 2003;52(15):336-340.Getis, A. (2008). A history of the concept of spatial autocorrelation: A geographer's perspective. Geographical Analysis, 40(3), 297-30929