Development of system for semi-automatic GIS visualization based on common data model OMOP-CDM : AEGIS (Application for Epidemiological Geographic Information.

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Presentation transcript:

Development of system for semi-automatic GIS visualization based on common data model OMOP-CDM : AEGIS (Application for Epidemiological Geographic Information System) Jaehyeong Cho, B.S.1, Seng Chan You, M.D. M.S.1, Kyehwon Kim, B.E.2, Doyeop Kim, B.E.1, Rae Woong Park, M.D., Ph.D.1,3 Dept. of Biomedical Informatics, Ajou University School of Medicine, Yeongtong-gu, Suwon Yeungnam University Graduate school of Medicine, Nam-gu, Daegu Dept. of Biomedical Sciences, Ajou University Graduate School of Medicine, Yeongtong-gu, Suwon

Introduction

*reference : World Health Organization GIS visualization Introduction *reference : World Health Organization

*reference : World Health Organization GIS visualization Introduction *reference : World Health Organization

*reference : World Health Organization GIS visualization Introduction *reference : World Health Organization

*reference : World Health Organization GIS visualization Introduction *reference : World Health Organization

*reference : World Health Organization GIS visualization Introduction *reference : World Health Organization

Transformation to OMOP-CDM Introduction OMOP-CDM (Observational Medical Outcome Partnership-Common Data Model) Data standardization system Global collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies Korean Hospital U.S. Insurance claims Other institute Transformation to OMOP-CDM Analytics tools Analyis tool Analyis tool Analysis result

Cohortmethod/ case control ATLAS / Achilles OMOP-CDM Introduction Analytics tools ATLAS Cohort definition Large-scale propensity score matching ACHILLES Profiling tool for database characterization and data quality assessment CaseControl, SelfControlledCaseSeries, etc. R packages for traditional observational study designs GIS visualization tools None Cohortmethod/ case control 그러나 GIS 시각화 툴은 없다 ATLAS – a web-based integrated platform for database exploration, standardized vocabulary browing, cohort definition, and population-level analysis: Software Tools Methods Profiling tool for database characterization and data quality assessment

AIM Introduction AEGIS development * AEGIS : Application for Epidemiological Geographic Information System Tools based on OMOP-CDM Semi-automated medical map generation

Method

GADM database Database of Global Administrative Area (GADM) Method Database of Global Administrative Area (GADM) spatial database of the location of the world's administrative areas Administrative areas : level 1-3 Korea USA level 1 : Nations level 2 : States Seoul Gyeonggi-do Illinois level 3 : Counties Gangnam-gu Suwon Springfield / Chicago

GADM database Database of Global Administrative Area (GADM) gadm@data Method Database of Global Administrative Area (GADM) gadm@data

GADM database Database of Global Administrative Area (GADM) Method Database of Global Administrative Area (GADM) gadm@polygons Autauga County, AL, United States •

Different regional classification systems GADM database Method How to mapping between GADM database and OMOP-CDM? CDM Location GADM Location Location_id Addres_1 Addres_2 … 11000 Alabama Autauga 11100 Baldwin 11200 Barbour 11300 Bibb NAME_1 ID_1 NAME_2 ID_2 NAME_3 ID_3 … USA 244 Alabama 1 Autauga Baldwin 2 Barbour 3 Bibb 4 Different regional classification systems Mapping table Location_id NAME_1 ID_1 NAME_2 ID_2 NAME_3 ID_3 11000 USA 244 Alabama 1 Autauga 11100 Baldwin 2 11200 Barbour 3 11300 Bibb 4

GADM database LEVEL 2 LEVEL 3 Processing result Method South Korea 213 NAME_1 ID_1 NAME_2 ID_2 COUNT South Korea 213 Seoul 16 538 Gyeonggi-do 8 583 LEVEL 3 NAME_1 ID_1 NAME_2 ID_2 NAME_3 ID_3 COUNT South Korea 213 Seoul 16 Gang-Seo 207 30 Gang-Nam 206 26 Gyeonggi-do 8 Suwon 100 50 Goyang 84 59

Cohort Options that visualize patient distribution ※Example Method Options that visualize patient distribution Absolute population Consider only the number of patients counted Proportion population Considers the denominator cohrt and the numerator cohort The observation period of the numerator cohort considers only patients within the observation period of the denominator cohort ※Example Patients with statin side effects Patients of dosing with statin

Result

Extracted cohort Extracted cohort Method To find out distribution of dialysis patients in Korea National Health Insurance Database National Sample Cohort Hemodialysis (HD) patients : Patients with ESRD, Patients with Hemodialysis From 2002 to 2013 n=2562 Peritoneal dialysis (PD) patients : Patients with ESRD, Patients with PD solutions From 2002 to 2013 n=393 Extracted cohort Extracted cohort *Switching is not considered

UI Result AEGIS UI

HD vs PD 2002-2013 HD Patients VS PD Patients (absolute) Result Hemodialysis (HD) patients : Patients with ESRD, Patients with Hemodialysis From 2002 to 2013 n=2562 Peritoneal dialysis (PD) patients : Patients with ESRD, Patients with PD solutions From 2002 to 2013 n=393

HD vs PD 2002-2013 HD Patients VS PD Patients (absolute) Result Hemodialysis (HD) patients : Patients with ESRD, Patients with Hemodialysis From 2002 to 2013 n=2562 Peritoneal dialysis (PD) patients : Patients with ESRD, Patients with PD solutions From 2002 to 2013 n=393

HD vs PD 2002-2013 HD Patients VS PD Patients (proportion) Result Hemodialysis (HD) patients : Patients with ESRD, Patients with Hemodialysis From 2002 to 2013 n=2562 Peritoneal dialysis (PD) patients : Patients with ESRD, Patients with PD solutions From 2002 to 2013 n=393

HD vs PD 2002-2013 HD Patients VS PD Patients (proportion) Result Hemodialysis (HD) patients : Patients with ESRD, Patients with Hemodialysis From 2002 to 2013 n=2562 Peritoneal dialysis (PD) patients : Patients with ESRD, Patients with PD solutions From 2002 to 2013 n=393

Result Example - US

Summary Result As a result, the development of AEGIS facilitates quick and effective analysis of geographical and temporal characteristics Medical maps generated through semi- automatic systems can be used to monitor disease In the future, we will provide additional functions to verify and visualize statistical differences in data

Suggestion and Question What we’re suggesting: Adopting GADM as standard vocabulary for geographical index in CDM Not in OMOP vocabulary table itself, but via using mapping table What we’re not sure: How to extract cohort for proportional visualization Should child cohort be included in mother cohort? As outcome cohort in target/comparator cohort Is it necessary to specify date in AEGIS (Isn’t it sufficient by using ATLAS?) Should we count only number of distinct person in cohort? Which statistical method can be used for geographical difference?

Future research

Future research We want to visualize and analyze differences in prescription patterns across the world after revision of guideline We can identify the impact of the guideline worldwide We can identify the differences in adoption of guideline within the nation and between the nations We want to visualize and analyze differences in mortality and complication rate of the disease (eg. MI, stroke )

Thank you