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SPATIAL ANALYSIS IN

HEALTH GEOGRAPHY

Eric Delmelle, Ph.D.

University of North Carolina at Charlotte

GIS Linkages and Computational Challenges

Geography and Earth Sciences

Center for Applied GIScience

Health Geography

1. Attempts to answer fundamental questions:

• Where and when do diseases tend to occur?

• What are the underlying processes?

• Why do such patterns exist?

2. Concept of “place” is critical to

understanding the health outcome

of an individual

2

residential mobility may

affect exposure

Geographical Information Systems

(GIS) and Health • Opportunities: Advances in GIS have allowed

analyses at finer levels of granularity

• Through overlay and analysis methods, identify the role

played by the environment at different scales

• Palette of application of GIS to health care

• Geocoding, travel modeling and accessibility

• Monitoring outbreaks of infectious diseases

• Visualizing space-time patterns of diseases

• New challenges

• Massive datasets (e.g. twitter, social network)

• Accuracy and privacy, quality (e.g. geocoding)

• Visualization of large database of space-time data

3

4

Geocoding >> travel estimation >> geographical accessibility

Children with birth defects in Florida

Objectives:

• Estimate travel impedance to hospitals for infants and children with

birth defects

• Research questions:

• Are there spatial disparities in travel impedance?

• What is the quality of the network model?

• What is/are the advantage(s) when using network distance?

1

Reference:

Delmelle, E. M., Cassell, C. H., Dony, C., Radcliff, E., Tanner, J. P., Siffel, C., & Kirby, R. S. (2013). Modeling travel impedance to medical care for

children with birth defects using Geographic Information Systems. Birth Defects Research Part A: Clinical and Molecular Teratology, 97(10), 673-684.

Who, When, Where?

5

1. Study design • Retrospective, state-wide, population-based cohort study of

Florida live-born infants with Spina bifida (SB) during Jan 1,

1998-Dec 31, 2007

2. Data sources

• Florida Department of Health

• Florida Birth Defects Registry (FBDR)

• Vital statistics

• Florida Agency for Healthcare Administration (AHCA)

• Hospital discharge data

1

6

Inclusion

Mother Florida resident

Birth defect ICD-9-CM codes

- Congenital heart defects (745.00-747.49) - N=45,144 - Neural tube defects (740.00, 740.10, 741.00- 741.93) – N=914 - Spina bifida without anencephaly (741.00-741.93) - N=669 - Chromosomal abnormalities (758.00-758.90), N = 4283 - Craniofacial anomalies (749.00-749.25, 744.01, 744.23, 756.00) N=7,468

Exclusion

Born out-of-state

Adoption/ prospective

adoption

6

Who, When, Where? 1

7

Spina bifida (SB) 1

• A complex, serious birth defect, resulting from an incomplete closure of spinal column early in fetal development

• Typically require lifelong, multidisciplinary care

• Face barriers to accessing health care, including transportation, costs and insurance

• Prevalence of spina bifida • ~1,500 per year in United States

• ~70 per year in Florida

• In Florida from Jan 1, 1998, to Dec 31, 2007 • N = 612

• Hospitalizations: N = 1,629 (1st year of life)

• Hospitals: N = 108

8

Geocoding of infants with SB 1

Two stages

Two levels

street

zip code

8

N=612

successfully

geocoded

9

Geocoding of infants and hospitals 1

9

Hospitals Maternal residence at delivery

9

Reference:



10

Hospitalizations and utilization 1

10

Geomasking

11

How to determine travel time? 1

Maternal residence at delivery

(origin)

Network (roads)

Origin-destination

(OD matrix) 11

Hospitals were child received care

(destination)

12

How to determine travel time? 1

(1) Built a network model

• Road network

• Network rules

• Quality assessment

(2) Computed one-way network

travel time and distance

Data Sources: Florida Birth Defects Registry (FBDR)/ Florida Department of Health, 1998-2007; Agency for Health Care Administration (ACHA), 1998-2008; Florida Department of Transportation

(2007); U.S. Census Bureau (2000)

(Source: ESRI)

12

13

Network modeling (rules) 1

Reference:



14

Network modeling (metric) 1

Hypothetical origin-destination route 14

• 40% deviation • 11% deviation

Percent deviation between Euclidean and network distance

• 92% deviation

15

Network modeling (quality) 1

Distance Time

Florida road Network 443.82 mi 6:46

Google Maps 446 mi 7:24

Hypothetical origin-destination route

16


Origin–destination

samples

urban

rural

Urbanized areas

and urban clusters (U.S. Census Bureau, 2000)

16

17


17

18

Impact of geocoding accuracy 1

Hypothetical origin-destination route

How does the GIS

algorithm snap a

geocoded location?

Very different results

Mapquest

19

Geographical accessibility 1

Theoretical one-way time traveled for

hospitalizations for infants with spina

bifida born in FL, 1998-2007

Theoretical one-way travel time to hospitals

used by infants with spina bifida born in FL,

1998-2007

(aggregated by county)

Reference:



20

Geographical accessibility 1

Reference:



21

Travel time results 1

One-way travel time N Percentage

≤ 30 minutes 345 56.4

> 30 minutes and < 60

minutes

130 21.2

> 60 minutes and < 90

minutes

59 9.6

> 90 minutes 78 12.8

1. Most hospitals within 30 minutes one-way driving time

2. Infants living in counties surrounding urban regions

experienced much shorter one-way travel time (≤ 30 minutes)

than infants living in rural counties

3. Range of one-way travel time for hospitalizations for infants

with spina bifida: from 2.4 to 494.1 minutes

Reference:



22

Concluding remarks 1

• Birth defects registry and hospital discharge data, combined with GIS

enable the examination of the spatial aspect of access to care

• Disparities exist in travel time and distance in Florida

• Network time and distance improve the estimation of travel

impedance compared to Euclidean distance

• The quality of the network model can be compared to on-line

resources

• Methodology can be applied to other populations of children with birth

defects and special health care needs

23

Dengue fever outbreaks, Colombia

Objectives:

1. Estimating space-time clustering of dengue fever outbreaks

2. Impact of uncertainty on detection of clusters

Geocoding | Uncertainty |

Space-time Clustering | Geovisualization

2

• Delmelle, E., Casas, I., Rojas, J. H., & Varela, A. (2013). Spatio-temporal patterns of Dengue Fever in Cali, Colombia. International Journal of Applied Geospatial Research, 4(4), 58-75.

• Delmelle, Eric M., et al. "A web-based geospatial toolkit for the monitoring of dengue fever." Applied Geography 52 (2014): 144-152.

• Hagenlocher, M., Delmelle, E., Casas, I., & Kienberger, S. (2013). Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based

modeling. International journal of health geographics, 12(1), 36.

• Eastin, M. D., Delmelle, E., Casas, I., Wexler, J., & Self, C. (2014). Intra-and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a

Tropical Environment in Colombia. The American journal of tropical medicine and hygiene, 91(3), 598-610.

• Kienberger, S., Hagenlocher, M., Delmelle, E., & Casas, I. (2013). A WebGIS tool for visualizing and exploring socioeconomic vulnerability to dengue fever in Cali, Colombia. Geospatial

health, 8(1), 313-316.

Context

• Vector-borne diseases (malaria, dengue fever)

spread very quickly under suitable conditions

• Prompt and accurate space-time analyses are necessary

to detect outbreaks in a timely manner and take

appropriate steps to curb expansion of the disease

• Accurate information should be disseminated among the

public to limit the risk of further contagion

24

Duncombe, J., et al., 2012. Review: geographical information systems for dengue surveillance. American Journal of Tropical Medicine and Hygiene, 86 (5), 753.

Eisen, L. and Eisen, R., 2011. Using geographic information systems and decision support systems for the prediction, prevention, and control of vector-borne diseases. Annual Review of Entomology, 56 (1), 41–61

Gubler, D.J. and Clark, G.G., 1995. Dengue/dengue hemorrhagic fever: the emergence of a global health problem. Emerging Infectious Diseases, 1 (2), 55..

2

Collecting data on dengue fever

• Dengue fever data from SIVIGILA (Public Health

Surveillance System). Implicit geographic information.

• Data for the year 2010

• n=11760 (9606)

25

• Delmelle, E., Casas, I., Rojas, J. H., & Varela, A. (2013). Spatio-temporal patterns of Dengue Fever in Cali, Colombia. International Journal of Applied Geospatial Research, 4(4), 58-75.

2

• Evaluate the impact of positional and temporal

inaccuracies on identifying outbreaks of dengue

fever

• Implement Space-Time Kernel Density Estimation

(STKDE) on both observed and simulated datasets

• Monte-Carlo simulations for statistical significance

• Parallel computing approach to reduce effort

• Visualize results in a 3D framework

26

Delmelle, E., Dony, C., Casas, I., Jia, M., & Tang, W. (2014). Visualizing the impact of space-time uncertainties on dengue fever patterns. International Journal of Geographical Information Science, (ahead-of-print), 1-21.

Objectives

2

• Spatial and temporal error

• Errors introduced during data collection and geocoding

will propagate in analysis, impact clustering tests

• Underestimation of local risk

• Misplacement of high-risk areas of a disease

• Misevaluation of spatial association

• Biased evidence for policy makers

• How does space-time uncertainty affect exploratory tests,

and how can space-time uncertainty be visualized?

27

• Jacquez, G.M., 1999. Spatial statistics when locations are uncertain. Annals of GIS, 5 (2), 77–87. 600

• Jacquez, G., 2012. A research agenda: does geocoding positional error matter in health GIS studies? Spatial and Spatio-Temporal Epidemiology, 3 (1), 7–16.

• Malizia, N., 2012. The effect of data inaccuracy on tests of space-time interaction. Transactions in GIS. 630

• Malizia, N., 2013. Inaccuracy, uncertainty and the space-time permutation scan statistic. PLoS One, 8 (2), e52034.

• Zimmerman, D.L., et al., 2007. Modeling the probability distribution of positional errors incurred by residential address geocoding. International Journal of Health Geographics, 6 (1).

Modeling space-time uncertainty 2

28

Framework 2

Space-time kernel density estimation

29

• Delmelle, E., Dony, C., Casas, I., Jia, M., & Tang, W. (2014). Visualizing the impact of space-time uncertainties on dengue fever patterns. International Journal of Geographical Information

Science, (ahead-of-print), 1-21.

2

Space-time kernel density estimation

• Extension of the kernel density through time.

• Input: set of space-time explicit data points

• Ouput: set of voxels where each voxel is a density

• 𝑥, 𝑦, 𝑡 : discretized set of voxels

• 𝑥𝑖 , 𝑦𝑖 , 𝑡𝑖 : location of space-time points

• 𝑘𝑠, 𝑘𝑡: kernel density function

30

• Delmelle, E., Dony, C., Casas, I., Jia, M., & Tang, W. (2014). Visualizing the impact of space-time uncertainties on dengue fever patterns. International Journal of Geographical Information

Science, (ahead-of-print), 1-21.

• Demšar, U. and Virrantaus, K., 2010. Space–time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24

(10), 1527–1542.

• Nakaya, T., and Yano, K., 2010. Visualising crime clusters in a space-time cube: an exploratory data-analysis approach using space-time kernel density estimation and scan statistics.

Transactions in GIS, 14 (3), 223–239.

𝑓 (𝑥, 𝑦, 𝑡) =1

𝑛ℎ𝑠2ℎ𝑡

𝐼(𝑑𝑖 < ℎ𝑠, t𝑖 < ℎt)𝑘𝑠𝑥−𝑥𝑖

ℎ𝑠,𝑦−𝑦𝑖

ℎ𝑠 𝑘𝑡(

𝑡−𝑡𝑖

ℎ𝑡)𝑖

3

Impact of parameters

• Impact of STKDE parameters on computational effort

31

2

Visualization

• The kernel density volume is rendered by estimating a

density value for each of the voxels

• Color-coding each voxel based on its density value (rainbow)

• Voxels with lower kernel density values are assigned a higher level

of transparency whereas higher densities are kept opaque

32

2

Estimating spatial uncertainty

• We measured GPS-based location of dengue fever cases

that were geocoded and estimated error in data

33

• Jacquez, G.M., 1999. Spatial statistics when locations are uncertain. Annals of GIS, 5 (2), 77–87. 600

• Jacquez, G., 2012. A research agenda: does geocoding positional error matter in health GIS studies? Spatial and Spatio-Temporal Epidemiology, 3 (1), 7–16.

• Zimmerman, D.L., et al., 2007. Modeling the probability distribution of positional errors incurred by residential address geocoding. International Journal of Health Geographics, 6 (1).

mean error length=66.4m;

median=56.4m,

range=0-273.6m.

2

Monte-Carlo simulations

• Space-time data are perturbed according to uncertainty

around the estimates

• Spatial coordinates are perturbed by a ∆d factor and

temporal coordinates by ∆t.

• The new coordinates are given by:

• {xi′, yi

′, ti′} = {xi ± α, yi ± β, ti ± ∆t}

34

{xi′, yi

′}

{xi, yi}

2

Parallel computation approach

• The derivation of STKDE of a point pattern consumes

considerable computing resources

• Conduct STKDE for each voxel

• Conduct STKDE on observed and simulated datasets

35

• Armstrong, M. P., and P. J. Densham. 1992. Domain decomposition for parallel processing of spatial problems. Computers, environment and urban systems 16 (6):497-513.

• Ding, Y., and P. J. Densham. 1996. Spatial strategies for parallel spatial modelling. IJGIS 10 (6):669-698.

2

Visualization on simulated sets

36

2

Discussion and Conclusions

37

• Dengue fever can take dramatic proportions when conditions (e.g., population, climate, behavior) are optimal

• We developed a spatial and temporal extension of the KDE algorithm to map spacetime clusters of dengue fever

• We perturbed each geocoded dengue fever case following a spatial and temporal error (Monte Carlo simulations)

• We conducted STKDE on both observed and simulated sets

• Significant clusters appeared different from one another • Compactness, length, reoccurrence and eradication

2

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Take-home message

Overall conclusions

39

• Explained two research projects that illustrate key concepts in

GIS when employed for health geography

• There are a number of limitations and assumptions:

• One-way travel time by car not accounting for congestion

• Maternal residency at delivery was the point of origin and remained

constant throughout

• Future research:

• Optimal placement of health

facilities to reduce travel burden

• Robust visualization techniques

GIS Computer Science

• Simulations (e.g. Monte Carlo)

• Complexity versus accuracy

• Dynamic visualization

CyberGIS

Overall conclusions

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Space-time patterns

Accessibility

Travel modeling

Geocoding

Accurate data

1

2

3

4

Pro

pag

atio

n o

f erro

r

Vis

ualiz

atio

n c

halle

ng

es

Acknowledgments

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Travel modeling for children with birth defects 1 Cynthia H. Cassell, Coline Dony, Elizabeth Radcliff, Russell S. Kirby, Jean Paul

Tanner, Sharon Watkins, Jane Correia, Chris DuClos, Csaba Siffel

Supported in part by Research Grant No. #5-FY09-533

from the March of Dimes

Dengue fever outbreaks 2 Wenwu Tang, Irene Casas, Meijuan Jia, Derek Marsh, Coline Dony, Alejandro Varelas

Contact information

42

Eric Delmelle, Ph.D.

Department of Geography and Earth Sciences and

Center for Applied GIScience

University of North Carolina at Charlotte

Charlotte, NC 28223

Tel: (704) 687-5991

Email: [email protected]

mailto:[email protected]

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