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1
Use of Geographical Information Systems (GIS) in Health Care
For: Northrop Grumman – CSSS Mentor Protégé Program TrainingPresenter: Naoru Koizumi, PhD
Assistant Professor, SPP, Associate Director, CSIMPPGeorge Mason University
Sep 2-3, 2010
Agenda – 3 Projects
NIH-K01 (completed)– Pneumococcal hot spot analysis in the Philadelphia area– Multi-hospital / individual level analysisMulti hospital / individual level analysis– K-function analysis
EPA (submitted)– Health effects of traffic-related air pollution in Delaware– K-function analysis & Dispersion modeling
NIH-R21 (started)– Geographic disparity of liver transplantation– Optimal boundaries of liver allocation– GIS & Discrete Event Simulation integration
NIH K01 P l P j tNIH-K01 Pneumococcal Project
2
Hot-Spot (K-Function) Analysis
Basic idea: Are there unusual number of events within a circle with a certain radius.
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Identifying Clustering: K-function Analysis
Hot-Spot (K-Function) Analysis
Lung and Larynx cancer in Lancashire, England
Lung
Larynx
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LARYNX CANCER?
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Hot-Spot (K-Function) Analysis
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Where do we see unusual clustering of Larynx cancer events?
Smoothing (Spatial Analyst Tool => Spatial Interpolation using the p-values
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3
Hot Spot Analysis of Pneumonia Cases Traffic Volume in VA
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Hot-Spot (K-Function) Analysis
NIH - K01 projectBlock level analysisAnalysis controlling for:
– Race, Age, HIV
Potential hot spot in certain areas in the Philadelphia area
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Philadelphia Area Pneumonia Cases
EPA f T ffi l t d Ai P ll ti P j tEPA of Traffic-related Air Pollution Project
Impact of Air Pollution on Health
No conclusive evidence of a direct causal relation between traffic generated pollution and health impact.Isolate the portion of traffic related pollution from theIsolate the portion of traffic-related pollution from the total particulate matter inventory in urban areasExisting literature in the field suggests that elderly and child populations are most vulnerable to air pollution. Elderly population is, in particular, susceptible to respiratory, cardiovascular and pulmonary diseases as a consequence of poor air quality
4
Air Pollution Data
Air Quality Management Section (AQMS) of the Delaware Department of Natural Resources and Environmental Control (DNREC)Environmental Control (DNREC)Four pollutants: PM0.27, hexavalent chromium, formaldehyde, and ozone61,776 aerosol size distributions were collected, 42,426 data points for formaldehyde and 26,843 for chromium.Spring 2005 (April 25–May 1), Summer 2005(July 31–August 6), Fall 2005 (November 10–14), and Winter 2006 (February 23–26)
Traffic and Pollution
Dispersion Modeling
Satellite picture of Wilmington, DE showing the driving route
Health Data and Analysis
Illness ICD-9 Code Illness ICD-9 CodeAcute bronchitis 466.0 Chronic obstructive pulmonary
disease491.21
Acute myocardial infarction 410 Chronic respiratory disease 519 9Acute myocardial infarction 410 Chronic respiratory disease 519.9Allergic rhinitis 477 Chronic rhinitis 472.0Allergies 995.3 Coronary artery disease 414.0Arterial narrowing 433 Coronary atherosclerosis 414.0Asthma 493.0 Coronary heart disease 414.0Atherogenesis 414.0 Eczema 692Atherosclerosis 440 Hay fever 477.0-477.9Atopic dermatitis 691 Lung cancer 162Bronchiolitis 466.1 Pneumonia (community-acquired) 486
Cardiovascular disease 429.2 Respiratory disease 519.9Cardiorespiratory distress 799.1 Systemic inflammatory response 995.90
Chronic bronchitis 491.2 Wheezing 786.07
5
Health Data and Analysis
Medicare Enrollment and Vital Statistics File between 1998 – 2008 (to determine1998 2008 (to determine length of exposure)Unique identifier, names and addresses of beneficiaries, county codes, zip codes, date of birth, date of death, sex, race, age, and monthly entitlement indicator (A/B/Both) Medicaid Service User Hot Spots in Philadelphia –
Illustration of K-fn Analysis
NIH R21 Li T l t P j tNIH-R21 Liver Transplant Project
Organ Allocation Problems
Disparities in Organ Transplant– Due to Race & Ethnicity: African Americans tend to
h l t t l t d it th i hi hhave low access to transplant despite their higher prevalence of Hepatitis C virus (or HCV).
Lack of insurance and delayed referral as potential causes.
– Due to Socio-economic StatusIndividuals living in higher income ZIP codes are more likely to receive transplantation.Individuals with a higher socio-econ status are more likely to be registered at multiple Organ Procurement Organization (OPO).
6
Organ Allocation Problems
Disparities in Organ Transplant (main focus)– Due to Geography – where candidates live
A candidate whose residence is close to a transplantation center tends to have a higher access. Mortality rates tend to be lower for those who live in the OPO service area with a higher number of transplants and larger population size.The access and the pre- and post- mortality rates differ significantly depending on the OPO at which the candidate is registered.
Background: Management Structure
– National Organ Transplant Act (NOTA,
Organ Allocation Management System
Transplant Act (NOTA, 1984) that regulated organ allocation process for the first time.
– NOTA authorized DHHS to contract with a non-profit entity (UNOS) to administer OPTN in 1986.
DHHS
UNOS
OPO1 OPO2 OPO43…
OPTN
Background: Organ Allocation Protocol
– Urgent cases receive priority– For candidates with the same urgency level, organs
are distributed first within OPO’s Donation Serviceare distributed first within OPO s Donation Service Area (DSA) and then if no candidate is found, within UNOS region
UNOS
DSAs
7
Current Liver Allocation System
Liver Allocation System
P i i
Within non-status 1 patients, a liver is offered in the descending order of MELD score and waiting time.Other factors include CIT, HLA, organ size, etc.
Donated liver
Patients in DSA
Patients in the DCA
Status 1 Patient in the US
Non-status 1 patients in the US
Status 1 patients
Non Status 1 patients
Patients in the UNOSregion
Patients in the UNOS region
Problems with the Current System
p1
Organs are NOT always allocated to the candidates
h t i d
Non-status 1 candidate
A Illustration: Organ Allocation Problem in Current System
p2
DSA1
UNOS Region1
who are most in need... Organs are getting wasted (48% of livers are declined by the first physicians).Persistent geographic disparities in both access and pre- /post- mortality rates across DSAs.
Status 1 candidate
Flexible CIT based Boundaries
""
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Flexible CIT based allocation boundariesBoundary estimate by picking up ZIP codes "
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ch
d*
d*d*
picking up ZIP codes Network Analyst (GIS) to estimate the approx travel time given an adequate transportation scheme, road configurations, traffic, etc.
8
Liver Allocation
Within the estimated boundary, assign an organ to a candidate who could minimize the score comprised of:score comprised of:– Average MELD score of the region– Average PNF% of the region– Average waiting time of the region– Regional gaps in “supply-demand” ratio
(# recipients / # candidates on the waiting list)
Multi objective function with a trade-off relationship Weighting method
Validation with Discrete Event Simulation (DES)
Given the boundary, use DES to simulate the consequences of various allocation scenarios.
Liver arrival
Transplant
DeathPost‐graft failure
Post‐graft failure / Death
ESLD
Death
Pre‐transplant state
Post‐transplant state
Candidate Transplant System
Allocation based on the “current” or “optimal” allocation system
Severity level
(Status 1 or not, MELD score)
Outputs
Performance Indicators showing:– Pre- /post- mortality rates
# of Stat s 1 patients recei ing a transplant– # of Status 1 patients receiving a transplant– Geographical (and racial) disparities– # of wasted organ
Integration of GIS and DES– Visual interface to test the consequences of various
boundary scenarios– Optimal allocation boundaries under certain
conditions