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

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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

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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