A Geospatial Modeling Approach to Public Health Research

Transcription

1 A Geospatial Modeling Approach to Public Health Research EunHye Enki Yoo Department of Geography, National Center for Geographic Information and Analysis (NCGIA), University at Buffalo, SUNY September 14, 2015

2 Outline 1 Challenges and opportunities of geospatial computing 2 Applications of geospatial approaches to public health studies Uncertainty-aware dynamic air pollution exposure assessments 2010/11 foot-and-mouth disease outbreaks in South Korea 3 New challenges and opportunities

3 Advancements in geospatial computing I 1 Technologies for location-based services and location-aware surveys and monitoring facilitated by the proliferation of web-based technologies, cell-phones, and social media, e.g., Google Earth, OpenStreetMap, Facebook check-in sensor-based environmental monitoring, i.e., micro-robots within human body for realtime and active health monitoring, cell-phone based surveillance and monitoring

4 Advancements in geospatial computing II 2 Big and complex geodata large volume of data, i.e., Landsat, surveillance camera, citizen science, twit data fast data with high velocity, i.e., real time sensing and sensor networks data with wide variety obtained from disparate sources at multiple resolutions, mixed with different types and quality 3 Statistical methods for geospatial data can use large, complex, and real time data to make discoveries and to solve real-world problems

5 Outline 1 Challenges and opportunities of geospatial computing 2 Applications of geospatial approaches to public health studies Uncertainty-aware dynamic air pollution exposure assessments 2010/11 foot-and-mouth disease outbreaks in South Korea 3 New challenges and opportunities

6 Outline 1 Challenges and opportunities of geospatial computing 2 Applications of geospatial approaches to public health studies Uncertainty-aware dynamic air pollution exposure assessments 2010/11 foot-and-mouth disease outbreaks in South Korea 3 New challenges and opportunities

7 Uncertainty-aware dynamic air pollution exposure assessments Dynamic Personal Exposure Estimation Home 20 Time (hour) Shopping 5 School Longitude Latitude Collaborators (I), NIH R21 (5R21ES017826) L. Mu and C. Rudra (Public health), M. Demirbas and A. Rudra (Computer science and engineering), and A. Szpiro (Biostatistics) Collaborators (II) W. Yao (Computer science and engineering), J. Castner (School of Nursing), V. Albert (Biostatistics)

8 Asthma exacerbations due to exposure Quality of life in US over 8% of the U.S. population with asthma nearly two million emergency department (ED) visits in 2011 Asthma Prevalence in 2010 [Source: 2012 Behavioral Risk Factor Surveillance System of CDC] 37% had asthma symptoms in the last 30 days (13% everyday) 24% had sleep disruption from asthma symptoms in last month 5% missed work or their usual activities due to asthma Increased visits to ED for asthma exacerbation and high concentrations of air pollution [Source: CDC Morbidity and Mortality Weekly Report, 2011]

9 Past research on air pollution exposure Population-based ecological studies Focus on aggregate and cumulative exposure and human exposure-response (Lioy 2010) a residence-based approach on a subpopulation, i.e., asthma patients, children, or pregnant women annual average of interpolated air pollutant concentrations PM2.5 annual design values [Source: EPA PM2.5 Designation Mapping Tool] The Asthma Surveillance Pyramid [Source: CDC]

10 Past and contemporary research on exposure Personal exposure assessments Are challenging because people tend to move about over time through changing pollution concentrations. biomarker analysis A personal sampler approach ambient air quality models microenvironment models

11 Past and contemporary research on exposure Traditional approach for time-activity patterns Primarily relied on questionnaire and self-reported travel diaries Burdensome to both investigators and participants Subject to complication and bias due to confusing questions or language barriers (non-native speakers) Incomplete due to recall errors, i.e., human memories Simplified description of geographical and temporal aspects of activities [Source: flachsbart 2007]

12 Past and contemporary research on exposure Conventional air pollution models 1 Ambient air quality model (Jerrett, 05) is based on averaged measurements artificially diffused on aggregated demographic data ignores individual mobility patterns oversimplifies the spatio-temporal variabilities in air pollutant concentrations 2 Microenvironmental (ME) model (Duan, 82) monitoring pollutants where the action takes place a total average exposure of a person (E) during the day E = J j=1 c j t j c j : microenvironment concentration at the j-th ME t j : the time the person spent in the corresponding ME ME types: outdoor, indoor, in-transit, workplace (Jantunen 98)

13 Uncertainty in exposure assessment Exposure misclassifications or measurement errors are likely to occur because model assumptions are unrealistic may affect exposure-health associations partially have been examined in biostatistics and epidemiological studies Little is known about how and to what degree the different types of error affect exposure misclassification and the precision of estimated health effects

14 Part I. Use of cellphone-based time-activity data for air pollutant exposure estimation Study aim: to design and demonstrate the feasibility of an efficient method to incorporate time-activity data into ambient air pollution models spatially and temporally resolved time-location data from GPS-equipped smart cellphones model estimation of individual exposures to fine particulate matter (PM 2.5 ) Significant results: positive and negative... high feasibility of using smartphone to collect personal level time-activity data varying temporal resolution across manufacturers, mobile networks, and the time of day that data collection occurred *NIH R21 ES017826, 07/ /2014

15 Study Design Ques1onnaire) Travel)diaries) GIS)database) GPS)tracking) Personal.ME$exposure.. model. Automated) ME)classifica1on)) me$me$acvies, Generalized.. linear.mixed.model. Error$simulated. exposure. es1mates. Ambient)air) pollutant)at) central)sites) ) ) ) ) ) SensorQbased)air) pollu1on) measurements) Spa1o$temporal. Kriging.model. ambient,air,quality, Indoor)air)model) ME$based,air,quality, Adverse)) health)effects) Relave,RISK,assessments, Frequency Relative Risk Distribution Relative risk

16 Data collection approximately 90 days of participation of 43 residents of the Buffalo-Niagara region during two phases (winter and summer of 2013) Time"ac3vitypaderns 24-h travel diaries on two randomly assigned days Traveldiary ID Date StartTime EndTime Loca3on Address City State A009 1/27/12 0:01:00 11:15:00 Home 406LinwoodAvenue Buffalo NewYork A009 1/27/12 11:35:00 23:59:59 Home 406LinwoodAvenue Buffalo NewYork A009 1/29/12 0:01:00 9:55:00 friend 130EmersonDrive Amherst NewYork A009 1/29/12 9:55:00 10:09:00 in"vehicle A009 1/29/12 10:10:00 10:25:00 Home 406LinwoodAvenue Buffalo NewYork A009 1/29/12 10:35:00 11:25:00 LasalleParkDogPark 101DartStreet Buffalo NewYork Baselineques3onnaire: age,gender,occupa3on, Time"loca3oninforma3on fromasmartphoneapp. Time x x 10 6 Longtitude Latitude time-location data using smartphone app. every five minutes

17 Data collection, cont. air quality data: fine particulate matter (PM 2.5 ) from three FRM stations and six NRM stations (daily and hourly observations) a Bayesian space-time downscaled model prediction

18 Simulation study design To what extent individuals dynamic time-activity patterns affect personal exposure estimates across different spatial and temporal variability in air pollutant fields? Spatial'process' smooth rough smooth rough Temporal'process strong strong weak weak Time/activity' static patterns' dyamic (A) (B) Time (hour) Time (hour) Latitutde Longitutde Latitude Longitude [Yoo, et. al (2015), Annals of the Association of American Geographers]

19 GPS-based time-activity patterns Accurate and detailed measurements of time-location records time-location data at fine resolution call for an automatic and efficient algorithm to transform raw GPS data to contextual information, time-me-activities i.e., ME type, geographic location and extent, and time of a day and duration Time (hour) Latitude Longitude

20 Routine time-activity patterns Density-based activity location detection i.e, Hourly activity patterns at 1 1 km 2 spatial resolution A map of occurrence likelihood at a given time of day i.e., where and how often an individual s activities occurred Possibly multiple activity locations depending on one s mobility and the time of a day Time (hour) Latitude Longitude

21 Air pollution concentration as a realization of a spatiotemporal process We buy information with assumptions Construct a model for a random field U(s) by adopting the following assumptions: 1 U(s) is a Gaussian Random Field 2 It is a stationary process whose probability distribution is invariant under location and time shifts 3 It is an isotropic process; probability distribution is invariant under rotation

22 Spatio-temporal block-to-point simulation Incorporate a spatial and temporal trend Accounts for support differences of data Reproduces spatial and temporal variability in air pollutant fields Takes into account redundancy and similarity among measurement data Assesses the uncertainty associated with spaiotemporal predictions

23 Geostatistical simulation of air pollution environmental conditions (A) (B) (C) (D) Latitude (A) Latitude (B) Latitude (C) Latitude (D) Latitude Longitude Latitude Longitude Latitude Longitude Latitude Longitude Longitude Longitude Longitude Longitude (E) 13 (F) 14 (G) 12 (H) temporal temporal profile profile (E) Time temporal temporal profile profile (F) Time temporal temporal profile profile (G) Time temporal temporal profile profile (H) Time

24 Personal exposure assessment Dynamic Personal Exposure Estimation 20 Home Time (hour) Shopping 5 School Longitude Latitude

25 Static vs. dynamic approach (A) (B) 20 P.1 20 P Time 10 Time Latitutde Longitutde Latitutde Longitutde (C) 0.2 (D) 0.9 Exposure estimates Uncertainty Time Time 0.5

26 Results: differences between static vs. dynamic exposure estimates dynamic exposure dynamic exposure (A) 11.5 MAD = static exposure (C) 11.5 MAD = static exposure dynamic exposure dynamic exposure (B) 11.5 MAD = static exposure (D) 11.5 MAD = static exposure *MAD: mean of the absolute differences Depend on the levels of autocorrelation of the ambient air quality surface Are largest in (A) spatially and temporally smooth air pollutant field and smallest in (D) lack of spatial or temporal autocorrelation. Are substantial when air pollutant fields are characterized by strong temporal autocorrelation

27 Discussion and Conclusions Conducted an uncertainty-aware personal air pollution exposure assessment, in which we assessed the effects of static vs. dynamic activity patterns of individuals spatial and temporal variability in air pollutant fields Developed and applied methods to characterize individuals mobility using a time-specific density-based activity location detection algorithm from GPS data simulate ambient air quality surfaces whose joint spatiotemporal variability is prescribed Demonstrated the difference between dynamic and static exposure estimates depends on the levels of autocorrelation of ambient air quality surfaces substantial when ambient air quality surfaces are spatially and temporally correlated minimal if the surface lacks autocorrelation

28 Outline 1 Challenges and opportunities of geospatial computing 2 Applications of geospatial approaches to public health studies Uncertainty-aware dynamic air pollution exposure assessments 2010/11 foot-and-mouth disease outbreaks in South Korea 3 New challenges and opportunities

29 Background Foot and Mouth Disease (FMD) a highly contagious viral disease that primarily affects cloven-hooved livestock and wildlife the most important livestock disease in the world : high infection rate, ease of spread through multitude routes of virus transmission, and direct economic impacts

30 2010/2011 FMD outbreaks Reemergence of FMD in S. Korea, since its last break in 1934 Nationwide spread despite government s control policy Substantial economic damages 304,952 deaths of animals due to culling, which costs 3 billion dollars (Kim 2011)

31 Introduction The spread of FMD epidemic is influenced by the several sources of spatial heterogeneity of exposure, animal density, and network Knowledge of the spatio-temporal patterns of FMD outbreaks is necessary for effective surveillance and control programs For our improved understanding, we aim to identify temporally and spatially localized peaks in the incidences evaluate the government s control policy in the FMD transmissions develop a Log Gaussian Cox process model for FMD outbreaks

32 Data: FMD outbreaks in S. Korea Primary infection at the pig farm in the province of GB A total of 205 livestock farms over 146 days (11/28/10 to 02/08/11) (A) (B) GG CB CN JN JB GN GW GB Town (Eum/Myun/Dong) City (Si) Km Province (Do) FMD Cases

33 Spatio-temporal point pattern analysis Do FMD incidences cluster in space as well as time? Hypothesis testing H 0 : complete spatio-temporal randomness, H a : space-time clustering The space-time scan statistics (Kulldorff, 1997) detect statistically significant spatio-temporal clusters with the information on the location and time intervals of specific clusters SatScan statistics (v. 9.4) Is there interrelations between any pair of point processes? Cross K-function evaluate the interaction between any pair of point process e.g., attraction, independence, or repulsion; space-time clustering

34 Spatio-temporal clusters of FMD outbreaks Table 1: Summary of Space-Time Clusters ID Start End Duration Infected Nearest Number of Date Date (days) Regions (do) Distance (km) Infected Farms C1 11/28/ /12/ GB C2 12/11/ /25/ GB, GG, GW C3 12/26/ /16/ GB, GG, GW, CB, CN C4 01/17/ /07/ GB, CN, GW C5 02/08/ /22/ GB *GB, GG, GW, CB, and CN denote for Gyoungsangbuk, Gyounggi, Gangwon, Chungchungbuk, and Chungchungnam-do, respectively.

35 Korean Government s movement restriction Was it effective? if so, when and how long?

36 Cross-K function analyses of FMD epidemics K-function within clusters, Figures (A), (B), and (C) cross K-function between consecutive clusters, (D), (E), and (F) (A) (B) (C) K 11(h) 0e+00 4e+10 8e K 22(h) 0e+00 2e+10 4e K 33(h) 0.0e e e h (meter) h (meter) h (meter) K 12(h) 0.0e e e+10 (D) iso K^I, J (r) K pois I, J (r) K 23(h) 0.0e e e+10 (E) K 34(h) 0.0e e e+10 (F) h (meter) h (meter) h (meter)

37 Log-Gaussian Cox process (LGCP) model for FMD epidemics An inhomogeneous Poisson process with stochastic intensity function Generate a spatially continuous map of FMD risk from observed point data Associate the FMD risk with spatially heterogeneous risk factors (A) (B) (A) livestock farm density, (B) the proximity to major highway

38 Log-Gaussian Cox process (LGCP) model Table 2: Posteiror Distribuion of parameters Mean Std. Dev quantile quantile (Intercept) Log (No. Farms) Distance to Highway Range 65,221 13,941 42,932 97,368 Std. Dev (A) (B) (A) Intensity of FMD incidences, (B) Spatial random effects

39 Discussion and Conclusions Examined spatial patterns of FMD transmissions in S. Korea during using spatial statistics the evolving pattern of FMD cases as a discrete-time sequence assessed the impact of control policies on FMD incidences successful government intervention policy ( repulsion in C1 & C2) a risk surface of FMD generated using a LGCP model accounting for the spatial heterogeneity of exposure and population at risk (livestock farms) incorporating stochastic effects by acknowledging our limited knowledge on the etiology of FMD and the lack of spatially and temporally resolved data.

40 Outline 1 Challenges and opportunities of geospatial computing 2 Applications of geospatial approaches to public health studies Uncertainty-aware dynamic air pollution exposure assessments 2010/11 foot-and-mouth disease outbreaks in South Korea 3 New challenges and opportunities

41 What s next? Ques1onnaire) Travel)diaries) GIS)database) GPS)tracking) Personal.ME$exposure.. model. Automated) ME)classifica1on)) me$me$acvies, Generalized.. linear.mixed.model. Error$simulated. exposure. es1mates. Ambient)air) pollutant)at) central)sites) ) ) ) ) ) SensorQbased)air) pollu1on) measurements) Spa1oQtemporal) Kriging)model) ambient,air,quality, Indoor)air)model) ME$based,air,quality, Adverse.. health.effects. Relave,RISK,assessments, Frequency Relative Risk Distribution Relative risk

42 Future Works Develop and implement a cost-effective embedded sensor system to collect real-time air pollutant concentrations indexed by time and location improve wearability validate sensor measurements develop efficient algorithms for sampling, storage, and analyses

43 Sensor-based personal exposure to PM :00 9 March Time 22:50 21:40 20:30 PM2.5 measurements : :10 home (cook) walk shopping walk home walk cafe walk home (sleep) lattitude longitude [Zhuang, Yan, et al.(2015) In the Proceedings of the 2015 Workshop on Pervasive Wireless Healthcare]

44 Future Works, cont. Improve the efficiency and accuracy of automatic ME classification algorithm Validate ambient air quality models Conduct coupled-model uncertainty assessments in human exposure and health studies by taking health outcomes from: ED use and hospitalization for asthma (aggregated health outcomes) personal monitoring for asthma patients enrolled in University Buffalo Clinics

45 Future Works, cont. Mining social network data for public health studies noisy and incomplete data a need of models that are scalable and dynamic 632,611 6,237 15,944,084 4,405,961 ctive users 2,535,706 ctive users 2,047 llness 57, ,739 31,874 data collected from NYC. Geoeir tweets relatively frequently Note that following reciprocity Figure 1: Visualization of a sample of friends in New York City. ith previous findings (Kwak [Sadilek, et et al.(2012) Predicting Disease Transmission from Geo-Tagged Micro-Blog Data] The red links between users represent friendships, and the colored pins show their current location on a map. We see the high- isited locations is calculated as ters) of the NYC grid that have lighted person X complaining about her health, and hinting about