Moving Beyond the Map Geospatial Analysis applied to Public Health and the Changing View of Health Equity Steve Sedlock, Executive Director The Virginia Network for Geospatial Health Research, Inc. Virginia Health Equity Conference – September 10-11, 2009

Geospatial Analysis applied to Public Health and the Changing View of Health Equity

Computer Mapping and Geographic Information Systems (GIS) Most common use – The creation of land records.* Other common uses – cartography, visualization, spatial data management. * 2008 Geospatial Technology Report, Geospatial Information & Technology Association (GITA), Aurora, CO.

Most common use – The creation of land records.*

Other common uses – cartography, visualization, spatial data management.

Computer Mapping and Geographic Information Systems (GIS) Analytical processes usually rank lower, with regard to usage. Spatial analysis Geostatistical analysis Network analysis This type of functionality is typically not available in core GIS (added purchase).* * ESRI extensions include Spatial, Geostatistical, Network, Tracking, and Survey Analysts. These are separate from the core ArcGIS Desktop functionality.

Analytical processes usually rank lower, with regard to usage.

Spatial analysis

Geostatistical analysis

Network analysis

This type of functionality is typically not available in core GIS (added purchase).*

Geographic Visualization Support material for reports, charts, and graphs. In order to justify the purchase of GIS software, an organization must get a higher level of usage. Simply as a support tool, GIS will have problems sustaining itself.

Support material for reports, charts, and graphs.

In order to justify the purchase of GIS software, an organization must get a higher level of usage.

Simply as a support tool, GIS will have problems sustaining itself.

Geospatial Analysis Providing an Expanded Use of GIS in Public Health Predictive Modeling – identifying “hot spots” where disease or problems are occurring. Time-distance analysis – identifying medically-underserved areas (MUAs). Service area analysis – determining the appropriate placement for clinics.

Predictive Modeling – identifying “hot spots” where disease or problems are occurring.

Time-distance analysis – identifying medically-underserved areas (MUAs).

Service area analysis – determining the appropriate placement for clinics.

Early Pioneer John Snow’s Cholera Map from 1885 Map with multiple layers showing a disease outbreak.

John Snow’s Cholera Map from 1885

Map with multiple layers showing a disease outbreak.

Predictive Modeling of Infant Mortality Example: City of Richmond In 2007, City rate of 12.4 deaths per 1,000 live births was well above the state rate of 7.7* Is infant mortality a geographically widespread problem in the city? Predictive modeling, through the use of geospatial process called Kringing, tells us “No.” * Source: Virginia Department of Health – Health Statistics Report, 2007.

Example: City of Richmond

In 2007, City rate of 12.4 deaths per 1,000 live births was well above the state rate of 7.7*

Is infant mortality a geographically widespread problem in the city?

Predictive modeling, through the use of geospatial process called Kringing, tells us “No.”

Richmond VA Infant Deaths, 1990-2005 Predictive Kriging Analysis

Predictive Modeling of Infant Mortality Example: City of Richmond Problem is more focused on an area in eastern Richmond and East-Central Henrico County. The problem is cross-jurisdictional. Reframing the answer to the problem: Potential for the city to geographically identify “hot spots” and focus resources. Next level of analysis –the social, behavioral, and environmental variables that could be contributing to this problem. Potential Intervention: Police know where the next murder is likely to take place. Predict where infant mortality is going to occur.

Example: City of Richmond

Problem is more focused on an area in eastern Richmond and East-Central Henrico County.

The problem is cross-jurisdictional.

Reframing the answer to the problem:

Potential for the city to geographically identify “hot spots” and focus resources.

Next level of analysis –the social, behavioral, and environmental variables that could be contributing to this problem.

Potential Intervention:

Police know where the next murder is likely to take place.

Predict where infant mortality is going to occur.

Infant Mortality – Optimum Clinic Placement Example: Tidewater area In 2007, Planning District 20 rate of 10.7* Norfolk – 16.4 Portsmouth – 7.3 Chesapeake – 12.3 Predictive modeling, using geospatial process called Kernel Density analysis, focuses in on an area that crosses between Norfolk, Tidewater, and Chesapeake. * Source: Virginia Department of Health – Health Statistics Report, 2007.

Example: Tidewater area

In 2007, Planning District 20 rate of 10.7*

Norfolk – 16.4

Portsmouth – 7.3

Chesapeake – 12.3

Predictive modeling, using geospatial process called Kernel Density analysis, focuses in on an area that crosses between Norfolk, Tidewater, and Chesapeake.

Infant Mortality – Optimum Clinic Placement Example: Tidewater area Mean-weighted center establishes the best locations for maternity clinics in the “hot spot” area. Next level of analysis -- the social, behavioral, and environmental variables that could be contributing to this being a “hot spot” area.

Example: Tidewater area

Mean-weighted center establishes the best locations for maternity clinics in the “hot spot” area.

Next level of analysis -- the social, behavioral, and environmental variables that could be contributing to this being a “hot spot” area.

Breast Cancer – Predicting Locations of Under-Reporting Example: Statewide Based on multiple analyses, determine the potential number of undiagnosed breast cancer cases.

Example: Statewide

Based on multiple analyses, determine the potential number of undiagnosed breast cancer cases.

! ( Mammography Units

Breast Cancer – Predicting Locations of Under-Reporting Example: Statewide Southern Virginia potentially has 2,000 undiagnosed cases of breast cancer. Mammography locations reflect a lack of facilities in this area. Next level of analysis -- the social, behavioral, and environmental variables that could be contributing to Southern Virginia’s potential under-reporting.

Example: Statewide

Southern Virginia potentially has 2,000 undiagnosed cases of breast cancer.

Mammography locations reflect a lack of facilities in this area.

Next level of analysis -- the social, behavioral, and environmental variables that could be contributing to Southern Virginia’s potential under-reporting.

Psychiatric Facilities – Drive Time Analysis Example: Statewide Areas within one hour of a psychiatric bed facility.

Example: Statewide

Areas within one hour of a psychiatric bed facility.

Psychiatric Facilities – Drive Time Analysis

Thinking and Planning Geospatial with Regard to Public Health Data distribution Confidentiality Cost and Economies of Scale Maintaining objectivity through independent research Reframing the question.

Data distribution

Confidentiality

Cost and Economies of Scale

Maintaining objectivity through independent research

Reframing the question.

Thank you! Moving Beyond the Map Steve Sedlock, Executive Director The Virginia Network for Geospatial Health Research, Inc. Phone: 804-264-3325 Email: [email_address]