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E-grāmata: Smart Use of State Public Health Data for Health Disparity Assessment

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  • Formāts: 328 pages
  • Izdošanas datums: 03-Sep-2018
  • Izdevniecība: Apple Academic Press Inc.
  • ISBN-13: 9781498786683
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Smart Use of State Public Health Data for Health Disparity AssessmentPalielināt
  • Formāts: 328 pages
  • Izdošanas datums: 03-Sep-2018
  • Izdevniecība: Apple Academic Press Inc.
  • ISBN-13: 9781498786683
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Health services are often fragmented along organizational lines with limited communication among the public healthrelated programs or organizations, such as mental health, social services, and public health services. This can result in disjointed decision making without necessary data and knowledge, organizational fragmentation, and disparate knowledge development across the full array of public health needs. When new questions or challenges arise that require collaboration, individual public health practitioners (e.g., surveillance specialists and epidemiologists) often do not have the time and energy to spend on them.

Smart Use of State Public Health Data for Health Disparity Assessment promotes data integration to aid crosscutting program collaboration. It explains how to maximize the use of various datasets from state health departments for assessing health disparity and for disease prevention. The authors offer practical advice on state public health data use, their strengths and weaknesses, data management insight, and lessons learned. They propose a bottom-up approach for building an integrated public health data warehouse that includes localized public health data.

The book is divided into three sections: Section I has seven chapters devoted to knowledge and skill preparations for recognizing disparity issues and integrating and analyzing local public health data. Section II provides a systematic surveillance effort by linking census tract poverty to other health disparity dimensions. Section III provides in-depth studies related to Sections I and II. All data used in the book have been geocoded to the census tract level, making it possible to go more local, even down to the neighborhood level.
Preface xiii
Acknowledgments xv
Authors xvii
Chapter 1 Enhanced Public Health Program Collaboration through Data Integration
1(14)
1.1 Introduction
1(1)
1.2 Data Integration at the National and State Levels
2(2)
1.3 Infrastructure Approach to Data Integration
4(2)
1.4
Chapter Highlights
6(5)
References
11(4)
Section I Conceptual, Analytical, and Data Preparations for Health Disparity Assessments
Chapter 2 Common Population-Based Health Disparity Dimensions
15(22)
2.1 Introduction
15(1)
2.2 Race and Ethnicity
15(5)
2.3 Gender
20(4)
2.4 Socioeconomic Status
24(6)
2.4.1 Occupation
25(1)
2.4.2 Income and Education
25(5)
2.5 Other Dimensions of Health Disparities
30(4)
2.5.1 Rural—Urban Health Disparities
30(2)
2.5.2 LEP Disparity
32(2)
2.6
Chapter Summary
34(1)
References
35(2)
Chapter 3 Common Public Health Data in a State Health Department
37(12)
3.1 Introduction
37(2)
3.2 Hospital Discharge Data
39(1)
3.3 Nebraska Cancer Registry Data
39(1)
3.4 Crash Outcome Data Evaluation System
40(1)
3.5 NTR Data
40(1)
3.6 Traumatic Brain and Spinal Cord Injury Registry
41(1)
3.7 Nebraska Parkinson's Disease Registry
41(1)
3.8 Nebraska State Immunization Information System
41(1)
3.9 Emergency Medical Services
41(1)
3.10 Enhanced HIV/AIDS Reporting System
42(1)
3.11 Nebraska Emergency Room Syndromic Surveillance
42(1)
3.12 Behavioral Risk Factor Surveillance System
42(1)
3.13 Vital Records
43(1)
3.14 Birth Defect Registry
43(1)
3.15 National Electronic Disease Surveillance System
44(1)
3.16 Nebraska Newborn Screening
44(1)
3.17 Pregnancy Risk Assessment Monitoring System
44(1)
3.18 Nebraska Adult Tobacco Survey and Social Climate Survey
45(1)
3.19 Nebraska WIC Program
45(1)
3.20 Pregnancy Nutrition Surveillance System
45(1)
3.21 Pediatric Nutrition Surveillance System
46(1)
3.22 Youth Risk Behavior Survey
46(1)
3.23 Nebraska Youth Tobacco Survey
46(1)
3.24 Nebraska Risk and Protective Factor Student Survey
47(1)
References
47(2)
Chapter 4 Data Linkage to Gain Additional Information
49(14)
4.1 Introduction
49(1)
4.2 Data Linkage Essentials
50(5)
4.2.1 Data File Assessments for Data Linkage
50(2)
4.2.2 Dealing with Large Dataset Blocking
52(1)
4.2.3 Determining Matching Quality: Weights
53(1)
4.2.4 Postmatching Evaluation
54(1)
4.3 Case Study: A Complete Linkage Process
55(4)
4.3.1 Data Standardization
56(1)
4.3.2 Design Blocking Variables
57(1)
4.3.3 Matching
57(1)
4.3.4 Postmatching Evaluation
58(1)
4.4 Other Issues in Record Linkage
59(1)
4.4.1 Training
59(1)
4.4.2 Privacy
59(1)
4.5
Chapter Summary
60(1)
References
61(2)
Chapter 5 Indexing Multiple Datasets: A Bottom-Up Approach to Data Warehousing
63(16)
5.1 Introduction
63(1)
5.2 Top-Down and Bottom-Up Approaches to Data Integration
63(7)
5.2.1 Case for Data Integration
63(3)
5.2.2 Top-Down Approach to Data Integration
66(2)
5.2.3 Bottom-Up Approach to Data Integration
68(2)
5.3 Piloting Bottom-Up Process to Gain Experience
70(4)
5.3.1 CVD Pilot Project: Proof of Technology
70(1)
5.3.1.1 Successes
70(1)
5.3.1.2 Lessons Learned
71(1)
5.3.2 Cancer Data Linkage Project: Proof of Concept
71(1)
5.3.2.1 Successes and Lessons Learned
72(1)
5.3.3 Linking PRAMS to Birth Data Seed Planting Pilot
72(2)
5.3.3.1 Lessons Learned
74(1)
5.4 Developing an Agency-Wide Strategy for MPI for Data Integration
74(3)
5.4.1 Integrating Pilot Projects
74(1)
5.4.2 Setting Up MPI with Loosely Connected Public Health Programs
74(2)
5.4.3 Connecting Databases through Federation
76(1)
5.5
Chapter Summary
77(1)
References
78(1)
Chapter 6 Using GIS for Data Integration and Surveillance
79(16)
6.1 Introduction
79(1)
6.2 Geocoding-Related Measures in Spatial Analysis
80(1)
6.2.1 Exact Point Measure for Individuals
80(1)
6.2.2 Physical Access Measure for Individuals
80(1)
6.2.3 Proxy Measure for Individual SES
80(1)
6.2.4 Multilevel Measures for Individuals
81(1)
6.3 Geocoding Strategies: Toward a Master Address Index
81(2)
6.4 Attaching Census Tract Data to Each Patient
83(3)
6.5 Spatial Visualization and Disease Surveillance
86(6)
6.5.1 Exploratory Spatial Data Analysis
87(5)
6.6
Chapter Summary
92(1)
References
92(3)
Chapter 7 Methodological Preparation for Health Disparity Assessment
95(22)
7.1 Introduction
95(1)
7.2 Setting the Surveillance Scope
95(2)
7.3 Study Design
97(1)
7.3.1 Cross-Sectional and Case Control
97(1)
7.3.2 Time Series or Pre- and Poststudy Design
97(1)
7.3.3 Longitudinal
97(1)
7.4 Cross-Sectional Measurements
98(7)
7.4.1 Rate
98(2)
7.4.2 Relative Rate
100(2)
7.4.3 Odds and Odds Ratio
102(1)
7.4.4 Standard Mortality Rate
102(3)
7.5 Intertemporal Measurements
105(7)
7.5.1 Seasonal Trends
105(4)
7.5.2 Intertemporal Trends
109(2)
7.5.3 Pre- and Post-Assessment
111(1)
7.6
Chapter Summary
112(1)
Appendix 7A: Cancer Cases for Selected Cancer Sites in Figure 7.4
113(1)
References
113(4)
Section II Health Disparity Surveillance Based on Hospital and Emergency Department Data
Chapter 8 SES Disparities in Hospitalization
117(20)
8.1 Introduction
117(1)
8.2 Analytical Approach to Neighborhood SES Disparity Assessments
118(2)
8.3 Surveillance Results
120(12)
8.3.1 SES Disparities in Comorbidities
120(2)
8.3.2 SES Disparities in Hospital Procedure Utilization
122(1)
8.3.3 SES Disparities in Hospital Readmission Surveillance
123(5)
8.3.4 Out-of-Hospital Mortality Surveillance by SES
128(4)
8.4 Concluding Remarks
132(1)
Appendix 8A: Rank Order of CCS Principal Diagnosis Category by Number of Discharges (2010 National Statistics—Principal Diagnosis Only)
133(1)
References
134(3)
Chapter 9 Sex Disparities in Hospitalization
137(12)
9.1 Introduction
137(1)
9.2 Using Hospital Incidence and Prevalence Data to Revisit the Morbidity—Mortality Paradox
138(3)
9.3 Using Prevalence Data to Assess Diseases More Common among Females
141(2)
9.4 Assessing Hospital Procedure Disparities
143(2)
9.5 Assessing Measurement Consistency
145(2)
9.6
Chapter Summary
147(1)
References
147(2)
Chapter 10 Rural—Urban Disparities in Hospitalization
149(18)
10.1 Introduction
149(1)
10.2 Our Approach to Model Rural—Urban Difference
150(2)
10.2.1 Distance to Hospital
151(1)
10.2.2 Rural Hospital Bypasser
152(1)
10.3 Rural—Urban Hospitalization Disparity Surveillance Results
152(6)
10.4 Case Study: Rural—Urban Injury Surveillance
158(4)
10.5
Chapter Summary
162(1)
Appendix 10A: Commodity-Based Rural—Urban Comparison
163(2)
References
165(2)
Chapter 11 Racial Disparities in Hospitalization
167(22)
11.1 Introduction
167(1)
11.2 Using Multiple Data Sources to Generate the Race Variable for HDD
167(2)
11.3 Patient-Based Assessment for Major Comorbidities
169(2)
11.4 Prevalence, Readmission, and Mortality for Major Hospitalizations
171(6)
11.4.1 Prevalence
171(2)
11.4.2 Readmission
173(3)
11.4.3 Mortality
176(1)
11.5 Case Study: Racial Disparity in Rehabilitation among Elderly AMI Patients
177(3)
11.6
Chapter Summary and Concluding Remarks
180(2)
Appendix 11A: Race Adjustment Strategies Using Census Data
182(3)
Appendix 11B: Companion Tables for Tables 11.2 through 11.4
185(2)
References
187(2)
Chapter 12 Using Emergency Department Data to Conduct Surveillance
189(16)
12.1 Introduction
189(1)
12.2 Influenza and Population Vulnerability
190(5)
12.3 Linking Weather Data to Hospital Data
195(3)
12.4
Chapter Summary and Concluding Remarks
198(1)
Appendix 12A: Original List of ILI Syndrome in Essence
199(1)
References
199(6)
Section III Data Integrations and Their Applications in Health Disparity Assessments
Chapter 13 Linking Cancer Registry Data to Hospital Discharge Data
205(12)
13.1 Introduction
205(1)
13.2 Method
206(2)
13.2.1 NCR Data
206(1)
13.2.2 Nebraska HDD
206(1)
13.2.3 Linkage Strategy
207(1)
13.2.3.1 Data Preparation
207(1)
13.2.3.2 Matching
207(1)
13.2.3.3 Manual Review
208(1)
13.2.3.4 Validation
208(1)
13.2.4 Treatment Augmentation Strategy
208(1)
13.3 Results
208(4)
13.3.1 Linkage Quality
208(1)
13.3.2 Potential to AID Case Finding
209(1)
13.3.3 Potential to Improve Radiation Treatment Information in the NCR
209(1)
13.3.4 Potential to Improve Radiation Treatment Information for Public Health
209(1)
13.3.5 Potential Use of Comorbidity Information
210(2)
13.4
Chapter Summary
212(1)
References
213(4)
Chapter 14 Mother Index and Its Applications
217(18)
14.1 Birth Certificate Data Linkage: A Brief Review
217(3)
14.1.1 LBW Paradox
217(1)
14.1.2 Intergenerational Data Files
218(1)
14.1.3 Birth Record Linkage to Medicaid Claims Data
219(1)
14.1.4 Other Data Linkages Based on Birth Certificate Data
219(1)
14.2 NMI and Its Applications
220(7)
14.2.1 Setting Up NMI
220(1)
14.2.2 Construction of Longitudinal Datasets for Descriptive Analysis
220(1)
14.2.3 Construction of Longitudinal Datasets for Multivariate Analysis
221(4)
14.2.4 Using NMI to Construct Longitudinal Datasets for PRAMS
225(2)
14.3 Using NMI and Geocoded Data to Construct Residential Mobility Information
227(2)
14.3.1 Residential SES Mobility
227(1)
14.3.2 Rapid Repeat Birth
228(1)
14.4
Chapter Summary
229(2)
References
231(4)
Chapter 15 Assessing and Managing Geocoding of Cancer Registry Data
235(8)
15.1 Introduction
235(1)
15.2 Geocoding Assessments
236(1)
15.3 Geocoding Workflow Development
237(2)
15.3.1 Geocoding within the ArcGIS Environment
237(1)
15.3.2 Geocoding Addresses from Other Sources
238(1)
15.3.3 Extract Census Tract Number for Each Geocoded Location
239(1)
15.4 Other Secured Internet Data Sources for Geocoding
239(1)
15.5 Concluding Remarks
240(1)
References
241(2)
Chapter 16 Sex Difference in Stroke Mortality
243(8)
16.1 Introduction
243(1)
16.2 Methods
244(1)
16.2.1 Data
244(1)
16.2.2 Statistical Analysis
245(1)
16.3 Results
245(3)
16.3.1 Descriptive Statistics
245(2)
16.3.2 Multivariate Analysis
247(1)
16.4
Chapter Summary and Discussion
248(2)
References
250(1)
Chapter 17 Model Outcomes of Acute Myocardial Infarction (AMI) by Residence and Hospital Locations
251(10)
17.1 Introduction
251(1)
17.2 Method
252(1)
17.2.1 Data Linkage
252(1)
17.2.2 Variables
252(1)
17.2.3 Statistical Analysis
253(1)
17.3 Results
253(4)
17.4 Concluding Remarks
257(1)
References
258(3)
Chapter 18 Disparities in Motor Vehicle Crash Injuries: From Race to Neighborhood
261(18)
18.1 Introduction
261(1)
18.2 Phase I Project: MVC Disparity Based on Police-Reported Injury Severity
262(5)
18.2.1 Methods
262(2)
18.2.1.1 At-Risk Population
262(1)
18.2.1.2 MVC Incidence Exclusion Criteria
263(1)
18.2.1.3 MVC Incidence Inclusion Criteria
263(1)
18.2.2 Results
264(2)
18.2.3 Discussion
266(1)
18.3 Phase II Project: Using MAIS for Hospital-Based Surveillance
267(4)
18.3.1 Method
267(1)
18.3.2 Results
268(2)
18.3.3 Discussion
270(1)
18.4 Phase III Project: Georeferencing MAIS-Based Injury Event to Census Tract for SES Analysis
271(4)
18.4.1 Method
271(1)
18.4.2 Results
272(2)
18.4.3 Discussion
274(1)
18.5
Chapter Summary and Concluding Remarks
275(1)
Appendix 18A: Sensitivity Analysis
275(1)
References
276(3)
Chapter 19 Linking Cancer Screening and Cancer Registry Data for Outcome Assessments
279(12)
19.1 Introduction
279(2)
19.2 Method
281(2)
19.2.1 Data Source
281(1)
19.2.2 Inclusion and Exclusion Criteria
281(1)
19.2.3 Variables
281(1)
19.2.4 Statistical Analyses
282(1)
19.3 Results
283(3)
19.4 Discussions and Conclusions
286(3)
References
289(2)
Chapter 20 Linking Environmental Variables to Parkinson's Disease
291(12)
20.1 Introduction
291(1)
20.2 Environmental and Disease Data Processing
292(1)
20.3 Cluster Detection and Exposure Comparison
293(3)
20.3.1 General Cluster Detection
293(1)
20.3.2 Focused Association Test
294(2)
20.4 Using Case-Control for Exposure Surveillance
296(2)
20.5 Conclusion and Discussion
298(1)
Appendix 20A: Results from SatScan Test and Associated Pesticides and Herbicides within and Outside of the Cluster
299(1)
References
300(3)
Index 303
Ge Lin is a professor of epidemiology in the School of Community Health Sciences, University of Nevada, Las Vegas. He is trained in spatial demography and geographic information systems. He is known for his work in spatial modeling, spatial statistics for count data, and spatial disparities in health. His most recent research focuses on the science of public health data. He uses the infrastructure approach to develop integrated data marts, data analysis utilities, and training modules for public health data specialists. He has been supported by several national and state organizations, including the National Institutes of Health.

Ming Qu is administrator of the Epidemiology and Informatics Unit, Nebraska Department of Health and Human Services (NEDHHS), which provides statistical, epidemiological, and geographic information services that support public health actions and policies. He previously was an injury epidemiologist and Crash Outcome Data Evaluation System administrator for the NDHHS, where he was instrumental in the development of the Nebraska Injury Surveillance System. Dr. Qu supervises functions of professionals and disease and injury surveillance, data collection and quality assurance, data analysis and reporting, data system development and evaluation. He is the author of numerous papers and book chapters.
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