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Transitions of Care Stroke Disparities Study All-TCSD-S Sites Annual Meeting August 6th, 2019
Transitions of Care Stroke Disparities Study
All-TCSD-S Sites Annual MeetingAugust 6th, 2019
Welcome and Introductions
Ralph L. Sacco MD MSProject Multi-PI
TCSD-S University of Miami Study Team
• Ralph L. Sacco, PI• Tanja Rundek, PI• Jose G. Romano, PI• Carolina M. Gutierrez, Research & Advocacy• Iszet Campo-Bustillo, Regulatory & Training Liaison• Hannah Gardener, Epidemiologist• Chuanhui Dong, Biostatistician • Antonio Bustillo, Analyst• Kefeng Wang, Data Manager• Erika Marulanda-Londono, Co-Investigator• Adina Zeki Al Hazzouri, Co-Investigator• Anny Rodriguez, Coordinator• Rory Robichaux, Sciera
TCSD-S Collaborating Sites and Teams
Current Participating Sites
• Baptist Jacksonville– Ricardo Hanel, PI– Mark Fafard, Coord
• Baptist Miami– Felipe de los Rios, PI– Josette Elysee, Coord
• Jackson Memorial– Jose Romano, PI– Anny Rodriguez, Coord
• Sarasota Memorial– Mauricio Concha, PI– Jeanette Wilson, Coord
• UF/Shands– Anna Khana, PI– Stephen Ruggles, Coord
• USF/Tampa– Scott Burgin, PI– Corbin Hilker, Coord
New Collaborating Sites
• Memorial Regional – Brijesh Mehta, PI– Erum Usman, Coord
• Memorial West– Brijesh Mehta, PI– Eduardo Cianferra, Coord
• Delray– Nils Mueller, PI– Donna Cabral, Coord
• Sacred Heart– Terry Neill, PI– Diane Tignor, Coord
• St Joseph Hospital Baycare Health– Sanjiv K Sahoo, PI– Shawna Miller, Coord
• Morton Plant Baycare Health– Ajay Arora, PI– Jena Botsford, Coord
Meeting Agenda1st Annual Transitions of Care Stroke Disparities-Study (TCSD-S) Meeting
Tuesday August 6th, 2019
1:45 - 1:55 Welcome and Introductions- Ralph L Sacco MD MS
1:55 - 2:10 TCSD-S Project Review- Goals and Objectives Methods –Jose G Romano MD
2:10 - 2:40 Data Updates– Tatjana Rundek MD PhDGWTG-S + REDcap Descriptive- Hannah Gardener ScDSocial Determinants of Health Data – Rory Robichaux – Sciera
2:40- 3:10 Recruitment, Challenges, and Strategies to Improve – Jose G Romano MDEnrollment Efficiency Log- Iszet Camp Bustillo, MD MPHStrategy for New Partnering Sites- Carolina Gutierrez PhDBest Practices towards Increased Enrollment – Erika Marulanda Londono MD
3:10- 3:30 Open discussion and Q/A - Ralph L Sacco MD MS
TCSD-S Project Review
Jose G. Romano MDProject Multi-PI
Background: Stroke recurrence and readmission
• 25% of all strokes are recurrent events1
• 18% of all Medicare readmissions cost $18B2
• After stroke, 25% readmitted within 30 days3
• In GWTG-Stroke, death and readmission after discharge 21% at 30 days4
• The drivers of readmissions are not well understood• Disparities in readmission exist and reasons for these
disparities are not well studied
1 Mozzafarian et al. Circulations 2016; 2 Medicare Payment Policy Report to Congress 2017; 3 Bravata et al. Stroke 2007; 4 Fonarow et al. Stroke 2011
Background: Readmissions after acute stroke hospitalization in FSR & CMS linked data• All-cause 30-day readmission was 15% (n=16,952)
– 14.4% for Whites (reference*)– 17.2% for Blacks: HR 1.19 (95% CI 0.99-1.44)– 16.7% for Hispanics: HR 1.02 (95% CI 0.87-1.20)– 14.7% for Others: HR 1.03 (95% CI 0.72-1.46)
• Median time d/c to readmission: 11 d • 23.9% readmissions due to stroke
– 16.6% IS or TIA– 1.5% ICH– 5.2% CEA/A&S
• 6.0% NHW, 1.8% NHB, 3.8% H, 7.5% other• 8.2% readmission due pneumonia or UTI
*Adjusted for demographics, comorbities, NIHSS, LOS, d/c destination
H Gardener et al. ISC 2017
Background: Long term outcomes after stroke in FLA FSR-CMS matched dataset
N=14,100Mean age=79±91-year mortality: 26%
12% Black73% White15% Hispanic
30-d aOR 1.286-mo aOR 1.231-yr aOR 1.16
*P<0.05 adjusting for age, sex, race/ethnicity, NIHSS
*P<0.05 adjusting for age, sex, NIHSS
TCSD-S Goals
Goal: Improve stroke outcomes and reduce readmissions
• Identify race-ethnic and sex disparities in hospital-to-home transition of care and outcomes after stroke.
• Identify the key stroke-related and social health-related determinants in hospital-to-home TOSC and stroke outcomes.
– Develop a Transitions of Stroke Care Performance Index
• Develop effective hospital-initiated system level initiatives to reduce disparities
Transition of Care Stroke Disparities Study, NIMHHD R01 MD-012467
TCSD-S Conceptual Design
StrokeHospitalization
Stroke-Related Factors
Transition of Stroke Care
Hospital to home
Stroke OutcomesReadmission and stroke outcomes
Socio-Economic & Environmental DisparitiesRace-Ethnic & Sex SDH
Multi-Modal Systems of Care InitiativesHealth System Feedback (Disparities Dashboard)
Health Care Provider Education
TCSD-S Initiative for TOSC Disparities
• TOSC Index will be developed in first 1,200 participants after which initiatives will be implemented to assess their effect on the TOSC-I and on outcomes.
• Feedback to sites on TOSC metrics, disparities, TOSC-I– Dashboard benchmarked against group
• Educational programs for hospital personnel involved in discharge and TOSC to improve outcomes– Creating multidisciplinary advisory group: patients,
caregivers, therapists, pharmacist, nutritionist, nurses, physicians
Data Updates
Tatjana Rundek, MD PHD Project Multi-PI
Acute Hospital
GWTG/FSR
• Demographics• Risk Factors & PMH• Premorbid status• Baseline meds• Arival mode, on/off time• NIHSS, symptoms• Treatment type & times• Disability (mRS) at DC• Education/counselling
Care Transitions
Interview at 30 days
Medication adherence• Filled stroke meds• Taking stroke medsLifestyle & behavior• Exercise, Diet• Tobacco/alcohol/drug
cessation treatmentRehabilitation• Attended therapy• Using DMEMedical attention• Scheduled follow-up• Seen by provider
Outcomes
Interview at 30, 90 days
• Hospital Readmission• Disability (mRS)• Stroke/TIA recurrence• Other CV events and
revascularization
Hospital charateristics• Region• Volume: Beds, stroke, tPA • Status: JC/DNV/HFAP
Public Sources/Sciera
Social Determinants• Community
characteristics• Household
characteristics
TCSD-S Data Sources
AHCA/JC/DNV/survey
TCSD-S Data Linkage Process
TCSD-S CRFs-Database
GWTG/FSR
Analysis: Disparities,
Predictors, TOSC-I
SDHSciera
Study ID GWTG ID
ZIP + 4
Master list
Data UpdatesGWTG-S + REDcap Descriptives
Hannah Gardener ScD
Goals
• Opportunity to elucidate how neighborhood/community characteristics can support or impede successful post-discharge stroke care– By linking GWTG/FSR data + post-discharge TCSDS
data + Sciera zip code-level data
• Review: Data elements, study population, current descriptives, future analytic opportunities
Data Linkage
TCSD-S CRFs-Database
GWTG/FSR
Analysis: Disparities,
Predictors, TOSC-I
SDHSciera
Study ID GWTG ID
ZIP
Master list
Merging Data by Zip Code
Zip Code Level Data• Population size• Race/ethnicity• House size (rooms)• SES (poverty, unemployment,
education)• Business counts/density (tobacco,
alcohol, restaurant, fast food, grocery, pharmacy, gym)
• Healthcare (hospital, clinic, rehab counts)
• Personal mobility (walk, bike, transit scores)
GWTG-S + Redcap Patient Data• Diagnosis• Followup interval• Modified Rankin Scale• Length of residence• Employment, education status• Social support and living situation• Rehospitalization• Post-discharge follow-up care• Rehabilitation• Medication use• Lifestyle modifications (diet,
exercise, tobacco cessation)• Mortality
Eligibility
• Acute ischemic stroke or intracerebral hemorrhage, age >18 • Discharge directly home• mRS 1 or greater at discharge• Patient or LAR signs informed consent-willing to take 2 f/u calls
Exclusion: • mRS = 0 (no residual symptoms, able to carry all activities)• TIA, SAH, Stroke NOS, elective admission for procedure• Children, prisoners
Goal:• 2400 patients /5 years
– 1200 to develop TOSC-performance index– Current N=64
GWTG-derived Variables• N=38• Ischemic N=31, ICH N=7• Age: mean=67±14, range=46-104, IQR=57-76• NIHSS: mean=5.9±5.4, range=0-21, IQR=2-8• Race/ethnicity: 24% white, 38% black, 38%
Hispanic
Redcap-derived variables
• Final Diagnosis (N=64)
• Baseline mRS (N=64) Day 30 (N=56) Day 90 (N=41) 0, 3.13
1, 40.63
2, 32.81
3, 12.5
4, 4.695, 6.25
ICH, 19
Ischemic stroke,
81
0, 33.93
1, 21.43
2, 21.43
3, 10.71
4, 8.93
5, 3.57
0, 29.27
1, 31.71
2, 19.51
3, 7.32
4, 7.325, 4.88
16% returned to ERor hospital
15% returned to ERor hospital
Redcap Variables• N=56• Did you fill your medication prescriptions
provided at discharge? Yes 95%• Have you modified your diet since your
stroke? Yes 59% (N=33)– Lower sodium N=26– Lower fat N=22– Lower carbohydrates N=23– More fruits/vegetables N=19– Vegetarian N=2– Avoid Vitamin K N=2
Redcap Variables (N=56)• Were you using tobacco, marijuana, excessive alcohol or
other drugs such as cocaine or amphetamines at the time of your stroke or within 1 year prior to your stroke? Yes N=6
Were you referred to a tobacco/alcohol/drug cessation clinic or support group? No N=6
Have you stopped using these substances?
Yes N=4No N=1Unsure N=1
Redcap variables descriptives• Was outpatient physical, occupational or
speech therapy prescribed? Yes 38%Are you currently attending these therapy
sessions?
• Are you walking on a treadmill or outside with the specific purpose of exercising? Yes 66%
• Have you been seen in clinic or doctor's office since hospital discharge?
yes73%
Appt scheduled
14%
No13%
yes24%
No52%
Completed, 24%
Redcap social variables (N=64)• How many persons do you know that you
feel close to?
• Who do you live with?
1 or 2, 9
3+, 91
20%
30%
1%
30%
19% alone
spouse/partner
sibling
children
other
Questions we can ask…• What are the neighborhood barriers to smoking
cessation?– Density of businesses selling tobacco?
• What are the neighborhood barriers to physical activity?– Poverty indices? Walkability score? Prevalence of gyms?
• What are the neighborhood barriers to clinic follow-up?– Poverty indices? Transit score? Clinic count?
• What are the neighborhood barriers to diet modifications?– Density of fast food restaurants? Density of grocery stores?
Neighborhood characteristics related to post-stroke lifestyle
0
1000
2000
3000
4000
5000
6000
7000
Yes (N=53) No (N=3)
Fill your medication Prescription
Mean Pharmacy Density
0
2
4
6
8
10
12
14
Yes (N=41) Appointment scheduled(N=8)
No (N=7)
Have you been seen in clinic or doctor's office
Mean clinic count
Neighborhood characteristics related to post-stroke lifestyle
0
2000
4000
6000
8000
10000
12000
Yes (N=33) No (N=23)
Modified Diet
Mean Restaurant Density
0
50
100
150
200
250
300
Yes (N=33) No (N=23)
Modified Diet
Mean Fast Food Density
Neighborhood characteristics related to post-stroke lifestyle
0
100
200
300
400
500
600
Yes (N=37) No (N=19)
Walking on treadmill or outside
Mean Gym Density
53.5
54
54.5
55
55.5
56
56.5
57
57.5
58
Yes (N=37) No (N=19)
Walking on treadmill or outside
Mean Walkability Score
Conclusions
• We will be able to relate post-stroke care and lifestyle modifications to neighborhood characteristics and resources.
• This information will be used to develop the TOSC PI.
• Will help identify and enhance utilization of valuable community resources and identify patients who may be at risk for low adherence to post-discharge recommended care.
Future Directions
• How do neighborhood characteristics impact long-term outcomes, mortality, hospital readmissions?– Do neighborhood characteristics interact with
other social determinants of health to determine long-term outcomes among stroke survivors?
Data UpdatesSocial Determinants of Health Data
Rory Robichaux – Sciera
Where big data tells big stories
University of MiamiTransitions of Care
Stroke Disparities Study (TCSD-S)
Improving Outcomes through the Application of
Social Determinants of Health (SDH) Data
8/06/2019
Sciera Confidential Data
Where big data tells big stories
Address Stroke Disparities and Improve OutcomesIn the TCSD-S through the
Identification, Acquisition, Validation, and Delivery of Relevant Social Determinants of Health (SDH) Data
SDH Data – Supporting TCSD-S
Sciera Confidential Data
Where big data tells big stories
Social Determinants of Health – FieldsCodes SDH Groups SDH Fields SDH Fields
STRUCTURAL INDICATORS
SI-1 % Homes Owner Occupied Total Housing Population Total Population
Total Housing Population Owner Occupied Total Housing Population Renter Occupied
BUILT ENVIRONMENT
BE-1 Walkability Index (1)Walk Score Bike Score
Transit Score
BE-2 Access to Recreational Facilities Gym Business Count Gym Business Density
BE-3 Hospitals, Clinics, and Rehabilitation Facilities
Hospital Count Distance of Each Hospital from Zip9
Clinic Count Rehabilitation Facility Count
BE-4 Pharmacies Pharmacy Business Count Pharmacy Business Density
RETAIL
R-1 Grocery Stores Grocery Business Count Grocery Business Density
R-2 Alcohol Outlets Alcohol Business Count Alcohol Business Density
R-3 Tobacco Outlets Tobacco Business Count Tobacco Business Density
R-4 Fast Food Restaurants Fast Food Business Count Fast Food Business Density
R-5 Full-Service Restaurants Full-Service Restaurants Count Full-Service Restaurants Density
Sciera Confidential Data
Where big data tells big stories
Social Determinants of Health – FieldsCodes SDH Groups SDH Fields SDH Fields
SOCIO-ECONOMIC INDICATORS
SEI-1 Neighborhood Poverty Percent Below Poverty
SEI-2 Racial and Ethnic Concentration
Percent Race White Percent Race Asian
Percent Race Black Percent Race Hispanic
Percent Race Native American
SEI-3 Urban vs. RuralRUCA
RUCA Primary Description RUCA Secondary Description
SEI-4 % Unemployment Unemployment
SEI-5 % Education Attainment Percent High School Diploma or Higher Percent Bachelors Degree or Higher
SEI-6 # Rooms per Household
Count 1 Room Count No Bedrooms
Count 2 or 3 Rooms Count 1 BedroomCount 4 or 5 Rooms Count 2 or 3 BedroomsCount 6 or 7 Rooms Count 4 or More BedroomsCount 8 or More Rooms
SEI-7 Household Economics (Zip9 Range) Household Income Home Value
INTERNET
I-1 Access to Internet - ResidentialMedian Max Download Speed Median Max Upload SpeedMax Download Speed Max Upload Speed
Sciera Confidential Data
Where big data tells big stories
Social Determinants of Health – Data
GEOGRAPHY POPULATION RACE/ETHNICITY
Zip Code City State Total Housing Population
Total Owner Occupied
Total Renter Occupied
Total Population
Percent White Percent Black
Percent Native
American
Percent Asian
Percent Hispanic
32162 The Villages FL 55,396 53,427 1,969 55,439 98% 1% 0% 1% 2%32601 Gainesville FL 16,729 5,452 11,277 18,182 69% 21% 0% 5% 9%33125 Miami FL 59,607 18,980 40,627 61,036 89% 7% 0% 1% 93%33324 Fort Lauderdale FL 47,469 26,861 20,608 47,588 75% 12% 0% 6% 30%33624 Tampa FL 37,955 24,927 13,028 38,139 78% 11% 0% 5% 32%
HOUSING SOCIO-ECONOMIC
Housing --1 room
Housing --2-3 rooms
Housing --4-5 rooms
Housing --6-7 rooms
Housing -->7 rooms
Housing --No Bedrooms
Housing --1 Bedroom
Housing --2-3 Bedrooms
Housing -->3 Bedrooms
Percent Below
Poverty
Unemploy-ment Rate
Education --Percent >= High School
Education --Percent >= Bachelors
Degree64 1,149 16,709 10,810 1,666 77 202 29,761 358 5.4% 3.9% 97.0% 40.6%
313 1,929 3,341 1,568 614 313 1,554 4,945 953 45.9% 10.0% 92.9% 47.7%1,747 6,665 9,289 2,299 356 1,785 5,289 12,364 918 29.4% 5.2% 65.3% 12.5%
311 3,108 9,663 4,021 1,805 339 2,518 13,568 2,483 9.7% 5.6% 94.8% 44.7%782 1,203 5,154 4,429 3,129 782 1,083 9,076 3,756 10.7% 6.4% 93.8% 36.3%
BUSINESS COUNTS/DENSITYTobacco Business
Count
Tobacco Business Density
Alcohol Business
Count
Alcohol Business Density
Restaurant Business
Count
Restaurant Business Density
Fast Food Business
Count
Fast Food Business Density
Grocery Business
Count
Grocery Business Density
Pharmacy Business
Count
Pharmacy Business Density
Gym Business
Count
Gym Business Density
2 33.3 2 33.3 51 848.4 9 149.7 8 133.1 6 99.8 0 0.0
8 679.7 8 679.7 76 6,456.9 4 339.8 2 169.9 4 339.8 7 594.7
11 1,085.0 2 197.3 82 8,088.1 0 0.0 6 591.8 52 5,129.0 0 0.0
11 462.5 6 252.3 117 4,919.5 13 546.6 15 630.7 6 252.3 14 588.7
0 0.0 1 43.0 27 1,161.1 0 0.0 1 43.0 1 43.0 3 129.0
Sciera Confidential Data
Where big data tells big stories
Social Determinants of Health – Data
GEOGRAPHY RESIDENTIAL INTERNET AVAILABILITY HEALTHCARE PERSONAL MOBILITY
Zip Code City State
Median Max
Download Speed
Median Max
Upload Speed
Max Download
Speed
Max Upload Speed
Hospital Count
Hospital Distance
Clinic Count
Rehab Count
Walk Score
(1)
Transit Score
(1)
Bike Score
(1)
32162 The Villages FL 100 10 1,000 1,000 0 3 0 0 58 32601 Gainesville FL 18 1 1,000 1,000 0 8 0 62 33125 Miami FL 18 1 1,000 1,000 1 TBD 26 0 76 33324 Fort Lauderdale FL 75 20 1,000 1,000 1 TBD 20 1 24 24 56 33624 Tampa FL 30 30 1,000 100 0 4 0 7 0 45
RURAL vs URBAN
RUCA RUCA Primary Description RUCA Secondary Description
1.1 Metropolitan area core: primary flow within an urbanized area (UA) Secondary flow 30% to 50% to a larger UA1.0 Metropolitan area core: primary flow within an urbanized area (UA)1.0 Metropolitan area core: primary flow within an urbanized area (UA)1.0 Metropolitan area core: primary flow within an urbanized area (UA)1.0 Metropolitan area core: primary flow within an urbanized area (UA)
Sciera Confidential Data
Where big data tells big stories
Social Determinants of Health – DataZip9-Level Household Economics
Zip Code(5-digit) Zip Code+4 Mode Household Income Range Mode Home Value Range
3312533125-1832 $10,000 - $14,999 $150,000 - $199,99933125-4339 $65,000 - $74,999 $125,000 - $149,99933125-4421 $35,000 - $39,999 $400,000 - $499,999
3313333133-2720 $45,000 - $49,999 $400,000 - $499,99933133-4606 $75,000 - $99,999 $150,000 - $199,99933133-4806 $35,000 - $39,999 $150,000 - $199,999
3314233142-5545 $25,000 - $29,999 $100,000 - $124,99933142-6617 $10,000 - $14,999 $400,000 - $499,99933142-8445 $45,000 - $49,999 $150,000 - $199,999
3314733147-1415 $10,000 - $14,999 $50,000 - $74,99933147-1810 $35,000 - $39,999 $200,000 - $249,99933147-7904 $10,000 - $14,999 $75,000 - $99,999
3316533165-2922 $150,000 - $174,999 $1,000,000+33165-6654 $75,000 - $99,999 $1,000,000+33165-8148 $75,000 - $99,999 $125,000 - $149,999
Note: Number of homes in each zip code+4 listed above range from 3 – 23Small number of homes in a zip code+4 may impact the viability of statistical mode provided
Sciera Confidential Data
Where big data tells big stories
Explanatory Notes
(1) – Walkability Index
Measured at the zip code level Each score is on scale 1 - 100
Walk Score – Measures suitability for walking with sidewalks, parks, pedestrian crossingswith traffic lights Score of <50: most or all errands require a car; “Car-Dependent”
Public Transit Score – Measures the proximity and availability of public transit Score of <50: some or minimal transit
Bike Score – Measures paths suitable for cycling, parks, crossings with traffic lights Score of <50: minimal bike infrastructure
Source: Walkscore.com
Sciera Confidential Data
Recruitment, Challenges, and Strategies to Improve
Jose G Romano, MD Multi-PI
Planned Enrollment
2400 patients /5 years – 1200 to develop TOSC-PI– 1200 to validate TOSC-PI, evaluate disparities, develop initiatives
to reduce disparities in TOSC
– Total enrolled to date: 145 (overall)– We expected to have 780 enrolled by year 2. We have just
entered year 3.
Recruitment, Challenges, and Strategies to Improve
Enrollment Efficiency Log
Iszet Camp Bustillo, MD MPHRegulatory & Training Liaison
TCSD-S Enrollment Efficiency Log
TCSD-S Regulatory update
• Current protocol v 3.0 April 01, 2019: Approved April 09, 2019• Eligibility criteria modified to remove the requirement of mRS ≥
1 at the time of hospital discharge.• Current MOO v05.05.2019 (Protocol version 3.0)• TCSD-S Continuing Review to cIRB due on 10/08/19.
• Please complete the Site CR documents and return them by or before Friday, August 19th.
• TCSD-S Protocol Deviation Log• TCSD-S HRP 812 Form-Site Continuing Review• TCSD-S Summary of the progress of the study protocol
(summary of the conduct of TCSD-S) at your site
Recruitment, Challenges, and Strategies to Improve
Strategy for New Partnering Sites
Carolina Gutierrez, PhDResearch and Advocacy Director
Increasing the number of sites = increasing data and enrollment
Process Use to Identify Potential/ New Collaborating Sites:
96
44
10
Hospital interested and pursuing joining project
Hospitals participating in the Florida Stroke Registry
Hospitals with >200 annual stroke discharges
Previous research experience and infrastructure
6
Recruitment, Challenges, and Strategies to Improve
Best Practices towards Increased Enrollment
Erika Marulanda Londono MD Co-PI
Best Practices• Site TCSD-S team will review every day a list of patients expected to be discharged
in the next 48 hours.• Clearly defined Roles and Responsibilities: • The clinical team (including fellows and residents at appropriate sites) will provide
the stroke coordinator a daily discharge list indicating “who is going home” or “potentially going home”.
• Stroke Coordinator communicates everyday with the clinical team (texting, attending rounds) and approaches potential participants on a daily basis.
• Increased Study Notifications for the clinical team including emails and enrollment updates at meetings.
• Increased Communication and Interaction between Study Sites:• Quarterly call with sites to include recruitment by site and review potential
barriers and share best enrolling practices.• Regular emails and use of social media to sites to share any news and enrollment
recognition.
Pending EML slides
Eligibility Revision to Enhance Enrollment
Exclusion: • TIA, SAH, Stroke NOS, elective admission for procedure• Children, prisonersChanges:• mRS = 0 not an exclusion- REVISED
Pending EML slides
Open discussion and Q/A
Ralph L Sacco, MD MS
UM TCSD-S Contact information• Iszet Campo-Bustillo 305-243-8018
• Carolina Gutierrez [email protected]
• Jose Romano [email protected]
• Tatjana Rundek [email protected]
• Erika Marulanda-Londono [email protected]
• Chuanhui Dong [email protected]
• Kefeng Wang [email protected]
