Spatial Clustering of Scleroderma in Three Michigan Counties

Slides:



Advertisements
Similar presentations
A Spatial Scan Statistic for Survival Data Lan Huang, Dep Statistics, Univ Connecticut Martin Kulldorff, Harvard Medical School David Gregorio, Dep Community.
Advertisements

What is Epidemiology? The study of the distribution and determinants of diseases and injuries in human populations. Source: Mausner and Kramer, Mausner.
Hepatitis B: Epidemiology
A Decision-Making Approach
Using SPSS for Inferential Statistics UDP 520 Lab 3 Lin October 30 th, 2007.
Meredith G. Hennon, MPH and the Supercourse team in Pittsburgh.
Spatial Statistics for Cancer Surveillance Martin Kulldorff Harvard Medical School and Harvard Pilgrim Health Care.
Arthritis Facts. Leading Causes of Disability Among U.S. Adults, 1999 Among U.S. Adults, 1999 Arthritis is the leading cause of disability in the United.
Assessing Spatial Autocorrelation of Intraoral Loss of Periodontal Attachment: A Demonstration Project Brent McDaniel Epid 624, Winter 1999 UM-SPH.
A Cluster of Hepatitis C among Rural, Young Adults – Illinois, 2012 Julia Howland, MPH CPH CDC/CSTE Applied Epidemiology Fellow Illinois Department of.
An Analysis of Childhood Asthma and Environmental Exposures in Utah Michelle Gillette, M.P.H. Office of Epidemiology Utah Department of Health.
Breaking Statistical Rules: How bad is it really? Presented by Sio F. Kong Joint work with: Janet Locke, Samson Amede Advisor: Dr. C. K. Chauhan.
t-tests Quantitative Data One group  1-sample t-test
SIMPLE TWO GROUP TESTS Prof Peter T Donnan Prof Peter T Donnan.
Spatial Clustering of Scleroderma in Three Michigan Counties “The Toledo Twins” Sharon HensleyAlford Sarah Ann Cleveland   
Quarterly HIV/AIDS Analysis for Michigan January 1, 2008 Michigan Department of Community Health HIV/STD/Viral Hepatitis and TB Epidemiology Section Division.
SEER Provided Data Mohammad Afnan Baqai 12/3/2009.
An Autoimmune Project by: Evan Moore and Courtney Blue Honors Anatomy and Physiology.
Scleroderma: Education is the Key. By, Dana Tanner.
My conflicts of interest during the last two years GSK has supported my participation in ERS congress 2010 Utrecht, September the 23th 2011.
CLINICAL MANIFESTATION OF SYSTEMIC SCLEROSIS
Statistical Significance: Tests for Spatial Randomness.
HIV Surveillance by Race/Ethnicity
Quantifying Health Benefits with Local Scale Air Quality Modeling Presentation to CMAS October 7 th, 2008 Bryan Hubbell, Karen Wesson and Neal Fann U.S.
Quarterly HIV/AIDS Analysis for Michigan January 1, 2007 Michigan Department of Community Health HIV/STD & Other Bloodborne Infections Surveillance Section.
Sarra Abdurrezag Esharik Systemic Lupus Erythematosus (SLE)
RESIDENTIAL MOVEMENT BETWEEN TIME OF CANCER DIAGNOSIS & DEATH Recinda Sherman, MPH, CTR Florida Cancer Data System.
Comparing Two Proportions Chapter 21. In a two-sample problem, we want to compare two populations or the responses to two treatments based on two independent.
Inferences Involving The Mean When  Is Not Known: One- And Two-Sample Designs Chapter 11 SHARON LAWNER WEINBERG SARAH KNAPP ABRAMOWITZ StatisticsSPSS.
Scleroderma.
Rory M Marks, John Z Ayanian, Brahmajee K Nallamothu
Senior Consultant, The Annie E. Casey Foundation
Introduction For inference on the difference between the means of two populations, we need samples from both populations. The basic assumptions.
Current or Former Smokers
2 Incidence SABER This module presents statistics from Chapter 2: Incidence Ontario Cancer Statistics 2016 Chapter 2: Incidence.
‘RETROSPECTIVE STUDY OF EFFICACY OF I. V
Slides from Amy Glasmeier G. P. Patil
Department of Sociology Population Studies Center
The Use of Census Data and Spatial Statistical Tools in GIS to Identify Economically Distressed Areas Presented by: Barbara Gibson & Ty Simmons SCAUG User.
HYPOTHESIS TESTS.
5 Prevalence ZEINAB This module presents statistics from Chapter 5: Prevalence Ontario Cancer Statistics 2016 Chapter 5: Prevalence.
Estimation & Hypothesis Testing for Two Population Parameters
Scleroderma Description: Scleroderma (Sclero= hardening, Derma=skin) is a chronic autoimmune disorder characterized by the hardening of the skin, shrinking.
Background. NCDMod: a microsimulation model exploring the economic impacts of obesity interventions in Australia.
Racial Disparity in Smoking-Attributable Mortality, Years of Potential Life Lost: Case of Missouri Noaman Kayani, PhD Chronic Disease and Nutrition.
Trends in Chronic Diseases by Demographic Variables, Hawaii’s Older Population, Hawaii Health Survey (HHS) K. Kromer Baker1, A. T. Onaka1, B. Horiuchi1,
Scleroderma May Cause Male Organ Problems
Environmental Modeling Basic Testing Methods - Statistics
PCORnet Modular Program 1
Kin 304 Inferential Statistics
Tract > 75% White Tract > 75% Black Tract > 25% Hispanic
Addressing address quality in public health surveillance data
Comparison between Kaplan-Meier survival estimates of Bristol aortic valve surgery patients and the Monte Carlo-based generated Kaplan-Meier curve using.
5 Prevalence Ontario Cancer Statistics 2016 Chapter 5: Prevalence.
SMOKERS NONSMOKERS Sample 1, size n1 Sample 2, size n2
Hypothesis Tests for Proportions
I. Statistical Tests: Why do we use them? What do they involve?
More urologists are needed
Salman Waheeduddin MD Rheumatology, Aurora Health Care.
Andrew Narva  American Journal of Kidney Diseases 
High Chronic Disease Burden Among U.S. Women
Beth Wallace, BSN, RN-BC, FNP-S Fairfield University Summer 2010
Day 63 Agenda:.
National Cancer Statistics in Korea, 2015
SMOKERS NONSMOKERS Sample 1, size n1 Sample 2, size n2
2017 HIV/AIDS Epidemiology profile Cleveland/cuyahoga county
Fig. 2. The pain score change in vitamin C group
Overview of Arthritis Brought to you in collaboration by:
Chapter 22 – Comparing Two Proportions
Lung cancer incidence for men and women in Sweden and Norway from 1960–1999 for age standardised rates per inhabitants based upon census population.
Presentation transcript:

Spatial Clustering of Scleroderma in Three Michigan Counties       Spatial Clustering of Scleroderma in Three Michigan Counties “The Toledo Twins” Sharon HensleyAlford Sarah Ann Cleveland      

Background The disease that “turns people to stone” Classifications Chronic, connective tissue disease Unknown cause Collagen accumulation in some organs Classifications Localized Systemic

Who has scleroderma Approximately 150,000 people in the United States 4 times more women than men

Symptoms May Include Raynaud’s Phenomenon Swelling of hands and feet Pain and stiffness of joints Thickening of the skin Kidney, heart, and lung involvement Oral, facial, and dental problems

Diagnosis Difficult Involvement of several specialists May take months to years

Rates in Study Area Prevalence 242 cases per 1million adults Incidence 19 new cases per 1 million adults per year Reference: Prevalence, incidence and survival rates of systemic sclerosis in the Detroit metropolitan area. Mayes et al.

Age Distribution of Cases

Sex Distribution 134 Females 37 Males 3.62 times more females than males

Etiologies UNKNOWN Possibilities include: silica dust vinyl chloride monomer pet ownership some solvents appetite suppressants

Significance To Explore... Environmental associations Spatial pattern  Disease process

Materials Disease Data Population Data Incidence data for 1989-1991 (N=171) Three counties: Macomb Oakland Wayne Population Data 1990 Census data Block and Tract divisions

Methods Gathering population data Geocoding cases Finding census data Extract pertinent information Geocoding cases ArcView Batch Match Digitizing unmatched cases

 Working with Unmatched Cases  Verifying address: US Post: www.usps.gov Using web map programs Mapquest: www.mapquest.com gives county lines Vicinity: www.mablast.com Digitizing

Matching Rate 161 Matched/166 Total 96% Matching Rate 171 Original Batch-Matched Addresses n=148 Non-matched n=18 Digitized n=13 Unable to Digitize n=5 5 Unmatchable Addresses n=166 161 Matched/166 Total 96% Matching Rate

Statistical Analysis Null Hypothesis: No spatial clustering Alter. Hypothesis: Spatial clustering Test statistic: Ipop, Moran’s I Statistical Program: STAT!

Macomb/Tracts STAT Output 4/13/1999 Assumption R Results from Ipop test Number of runs : 99 Ipop calculations Areas (m) : 194 cases (n) : 30 Population (x) : 724110 Ipop : -0.0000099 Ipop' : -0.2377846 E[I] : -0.0000014 % within : 99.9752702 % among : 0.0247298 Assumption R Variance : 0.0000000 z-score : -0.3175953 Significance : 0.7507920 (2-tailed) Approximation z-score : -0.3121641 Significance : 0.7549158 (2-tailed) Simulation Significance : 0.8800000 (2-tailed)

Macomb/Block STAT Output 4/14/1999 Results from Ipop test Number of runs : 99 Ipop calculations Areas (m) : 661 cases (n) : 30 Population (x) : 724112 Ipop : -0.0000208 Ipop' : -0.5008514 E[I] : -0.0000014 % within : 99.9957345 % among : 0.0042655 Assumption R Variance : 0.0000000 z-score : -0.3930764 Significance : 0.6942630 (2-tailed) Approximation z-score : -0.3855452 Significance : 0.6998335 (2-tailed) Simulation Significance : 0.6400000 (2-tailed)

Oakland/Tracts STAT Output 4/13/1999 Results from Ipop test Number of runs : 99 Ipop calculations Population (x) : 1101540 Ipop : 0.0000033 Ipop' : 0.0817093 E[I] : -0.0000009 % within : 99.8772695 % among : 0.1227305 Assumption R Variance : 0.0000000 z-score : 0.2045747 Significance : 0.8379044 (2-tailed) Approximation z-score : 0.2022084 Significance : 0.8397538 (2-tailed) Simulation Significance : 0.5600000 (2-tailed)

Oakland/Blocks

Wayne/Tracts STAT Output 4/13/1999 Results from Ipop test Number of runs : 99 Ipop calculations Areas (m) : 632 cases (n) : 86 Population (x) : 2159815 Ipop : -0.0000062 Ipop' : -0.1565116 E[I] : -0.0000005 % within : 100.4700628 % among : -0.4700628 Assumption R Variance : 0.0000000 z-score : -0.3578355 Significance : 0.7204664 (2-tailed) Approximation z-score : -0.3542334 Significance : 0.7231640 (2-tailed) Simulation Significance : 0.9200000 (2-tailed)

Wayne/Blocks

Discussion Limitations of Analysis Future Analysis Position uncertainty Residential history Reliability of census data Future Analysis Stratification by age, sex, race 3 county combination analysis Space/Time Analysis

First Honors: Andy Long Thank You First Honors: Andy Long Mark Wilson Dr. Mayes Geoff Jacquez