Cheng-Chia 'Brian' Chen - Research Portfoliocchen236/pdf/Research_Portfolio.pdf · Chen, Research Portfolio 5 people who walk to work) are associated with longitudinal trajectory

R e s e a r c h Po r t f o l i oCheng-Chia “Brian” Chen

PhD CandidateSchool of Public Health

Indiana University Bloomington Email: [email protected]

TABLE OF CONTENTS

Research & Consulting Experience

Research Statement

2. Research2. Consulting

4. Interests4. Statement

9. Dissertation Abstract10. Restricted Data Application Process Timeline

13. Presentations/Publications

16. Current Project17. Rural Home Healthcare Need Analysis Project19. Obesity & Fast Food Restaurants Project

22. Statistics22. GIS

Dissertation Related

Publications/Presentations

GIS Portfolio

1

3

8

12

15

Proficiency 21

Chen, Research Portfolio 1

Research & ConsultingExperience

Research and Consulting Experience


Research Associate 2013-2014Indiana Prevention Resource Center, Indiana University, Bloomington IN

• Responsible for spatial analysis using GIS data from the U.S. Census • Constructed maps based on network analysis using ArcGIS • Developed survey questions • Completed the final technical report • Applied mixed methods in a rural area

Research Analyst/Statistician 2011-2013The President’s Challenge, Indiana University, Bloomington IN

Consultant 2010-2011Taipei Veterans General Hospital, Taipei, TaiwanProject: “Relationship of Subjective Sleep Quality and Cardiac Autonomic Nervous Sys-tem, The Journal of The North American Menopause Society”

Statistical Consultant 2010Project: “Recreational Use of Trail Analysis,” Indiana University, Bloomington, INProject: “Life Satisfaction of Old Adults,” Indiana University, Bloomington, IN

• Prepared sample size, statistical analysis plans • Organized and developed statistical designs for new studies • Assisted with methods and models for statistical analysis • Assisted interpretation of experimental results • Provided statistical result writing consultation for the preparation for publication • Solved empirical issues and improve quality with theoretical needs

Research Assistant 2008-2011Indiana University, Bloomington INProject: “Partnership with China to Improve School Physical Activity Programs and Policies. National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH) (R21: PA-09-141)”

• Developed research protocols • Helped in writing grant proposals • Organized and facilitated collaborative and brainstorming meetings & writing

sessions

Research Assistant 2007-2008Center for Sport Policy & Conduct, Indiana University, Bloomington IN

• Analyzed and compared sport policies among 12 countries and conducted longitu-dinal research

• Interviewed top management personnel and policy specialists at national, state, and local agencies (e.g., President’s Council on Physical Fitness & Sports, Dept. of Health & Human Services, National Park & Recreation Association, YMCA of the USA & Greater Indianapolis)

• Reviewed the policy guidelines, by-laws regarding health, nutrition, and physical activity for further analyses


Research Statement

Research Statement


Major: • Social Determinants of Obesity • Health Policy • Geriatric Health and Well-being • Health Behavior • Evaluation of Physical Activity Interventions

Statement

Interests

My research interests fall broadly within multiple areas of public health. I am espe-cially interested in longitudinal studies and policy analysis for public health related topics such as obesity research, health policy, community health, social epidemiology, and health behavioral research. More specifically, my research has followed two distinct paths, and each path relates to the topics listed above. The first path is obesity-related research, such as longitudinal state-level socioeconomic and environmental effects on change in body mass index. The second path is health policy analysis. For instance, research topics in this area can be analyses of the Medicare Part D implementation and its health impact or examination of effect of the Affordable Care Act on diabetes screening utilization.

1. Obesity Related ResearchCurrent WorkTitle: Longitudinal State-Level Socioeconomic and Environmental Effects on Change in Body Mass Index This research focuses on analyzing the trends of individuals’ body mass index (BMI) growth among U.S. middle-aged and older adults. From the literature, people aged 40 and above show the highest obesity rate compared with that of other age groups regardless of sex and race/ethnicity.1 A strong body of evidence shows that clinical consequences of overweight and obesity, such as type 2 diabetes, are more serious and particularly problematic for elderly individuals.2 Obesity coupled with aging increased healthcare services and expenditure.3 Thus, a better understanding of obesity determinants for the middle-aged and older adults using a longitudinal design, especially socioecological obesogenic factors, is necessary to help reduce the high obesity prevalence and alleviate the overall burden of obesity-related diseases and associated health care costs. Given the aforementioned reasons, there is a strong need to investigate BMI trajectories among middle-aged and older adults. The hypotheses of the research include: (1) individual longitudinal trajectory patterns of BMI differ by sex in a sample of adults aged 50 years or older, (2) for both sexes, BMIs tend to increase over time, and (3) for both sexes, the three state-level variables (i.e., state-level SES, density of fast food restaurants, and proportion of

Minor • Biostatistics • Applied Statistics

Research Statement


people who walk to work) are associated with longitudinal trajectory patterns of BMI. Data used in this study are drawn from the Health and Retirement Study (HRS) for 2000-2010 and the 2010 U.S. Census. I link the HRS data to the 2010 U.S. Census data based on the census tract information of HRS respondents. The linkage allows me to investigate the impact of built environment on individuals’ BMI trajectories. I use three-level hierarchical linear models to analyze the trend of BMI with the different levels of impact from temporal effects (time-varying variables at Level 1), individual sociodemo-graphics (time-invariant variables at Level 2), and state-level characteristics such as SES (Level 3). My research contributes to the literature on individuals’ weight change by shedding light on the heterogeneity of BMI trajectories not only in the cross-sectional setting, but also in the longitudinal trend during the transition to older adulthood between different sexes, sociodemographics, and state-level environmental factors of weight gain. This study is the first to investigate BMI trajectories using a three-level multilevel modeling approach and targeting effects from intra-, inter-person, and between-state level variables sepa-rately with a capability to detect any cross-level interactions. In addition, only people who lived in the same census tract were included in my study, which controlled confound-ing environmental exposures. Thus, I am able to provide strong evidence regarding the socioeconomic and environmental effects on the patterns of individuals’ BMI trajectories. In brief, individuals’ BMIs are still growing for both sexes and state-level SES have greater influence on women’s weight gain. Moreover, environmental factors in this study did not make a difference for BMI trajectories. Obesity intervention efforts should focus more on individual obesity risk factors with the consideration of sex differences in mind.

Future WorkFor the obesity-related research, there are still plenty of unanswered questions. The ongo-ing research plans are as follows:1.I have started to evaluate the impact of built environments on changing weight patterns through different analytic geographic units such as counties, metropolitan statistical areas (MSAs), or census tracts. Individuals’ BMI trajectories may vary based on different geo-graphic units.2. Additional geographic information is used to identify another suitable built environ-ment variables (e.g., walkability index that is derived from land use, street connectivity and resident density, etc.) for further analyses.3. Multilevel analyses will be applied to different age groups using different longitudinal datasets.

2. Health Policy AnalysisCurrent WorkTitle: Medicare Part D Implementation and Associated Health Impact among Older Adults in the United States This research investigates potential positive health impacts, in addition to cost-sav-ing and increased drug adherence, due to Medicare Part D (Part D). From the literature

Research Statement


review, many studies shows that the implementation of Part D is associated with reduced out-of-pocket expenses, increased medication use, and improved adherence to essential medications for older adults.4, 5 Nevertheless, there is very limited literature in the change of health outcomes and status that could be associated with this health policy. One of the challenges is to find available sensitive health data to conduct such analyses. Thus, I have completed an extensive research proposal to apply for permission from the HRS and Cen-ters for Medicare and Medicaid Services to use restricted data including HRS-Medicare linked claims data. The main research question is to investigate if Part D enrollees have better health outcomes (e.g., change in self-rated health) than people who don’t enroll in Part D after two years of its implementation. Panel ordered logistic regression is used with the difference-in-differences approach for this study. This study has an important contribution to the knowledge of positive health impacts due to Part D implementation. To the best of my knowledge, this study is one of the first studies to investigate potential effects of Part D on variations of health outcomes and sta-tus of the Medicare beneficiaries. The results suggest that the implementation of Part D is associated with the decreased chance of having a worse overall health. This result is a crit-ical and encouraging message for public health professionals and policy makers because Part D enhances people’s ability to maintain their health and provides affordable options.

Future Work For the health policy analysis research, the application of the newly available data from the Center for Medicare and Medicaid Services has been carried out. With more years of the claims data, some health impacts like status of chronic diseases or men-tal health may need more time to reveal significant change. In addition to the research agenda for the Medicare Part D, policy analysis on the Affordable Care Act (ACA) is another research area I will conduct a series of research projects. The effectiveness of the ACA on preventive care (e.g., diabetes screenings for adults with high blood pressure) can provide valuable information for health policy planners. The impact of my current and future research can help understand how serious obe-sity influences people’s health and why it is so difficult to have effective weight controls. The policy analyses not only help researchers and stakeholders to further acknowledge how these services are utilized, but provide policy makers evidence of their effectiveness and reference for future adjustments.

References1. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the dis-tribution of body mass index among US adults, 1999-2010. JAMA. 2012;307(5):491-497.2. Han T, Tajar A, Lean M. Obesity and weight management in the elderly. BMJ. 2011;97(1):169-196.3. DeCaria JE, Sharp C, Petrella RJ. Scoping review report: Obesity in older adults. Int J Obes. 2012;36(9):1141-1150.


4. Donohue JM, Zhang Y, Lave JR, Gellad WF, Men A, Perera S, Hanlon JT. The Medi-care drug benefit (Part D) and treatment of heart failure in older adults. Am Heart J. 2010;160(1):159-165.5. Lichtenberg FR, Sun SX. The impact of Medicare Part D on prescription drug use by the elderly. Health Aff. 2007;26(6):1735-1744.





Longitudinal Analyses of Obesity Determinants and Associated Health Outcomes in the U.S. Middle-Aged & Older Adults

Dissertation Title:

Proposal Abstract:

Obesity has become a public health epidemic in the United States; the obesity rate among adults is 35.7 percent in 2009-2010. While an increase in obesity rates is preva-lent across all racial/ethnic groups, genders, educational levels, and geographic regions, adults aged 40 years or older show the highest obesity rates in terms of age groups. With the rapid growth of the aging population, high rates of obesity and consequent chronic diseases will continue to contribute to increased healthcare expenditure.

Literature is inconclusive on the association between individuals’ sociodemographic characteristics and obesity. Contradictory influences of gender, race, and ethnicity on obesity in adults have been reported. For instance, Black women were found heavier at a baseline and gained more weight than White women. On the other hand, other research-ers concluded that Black women had a slower increase in body mass index (BMI) than White women. Additionally, very few longitudinal studies have examined the influence of both individuals’ sociodemographic characteristics and neighborhood attributes on obesity. Finally, little is known about the health impact of Medicare Part D on quality of life among obese older adults (65 years and older).

Based on the gaps in the literature, this dissertation research is proposed to address two main research objectives. The first objective (Study #1) is to examine the association of both individuals’ sociodemographic characteristics and neighborhood attributes with the trajectory of BMI from 2000 to 2010 among middle-aged and older adults in the United States. The second objective (Study #2) is to evaluate the health impact (i.e., life satisfaction, comorbidity, mental health, and self-rated health) on the elderly due to the implementation of the Medicare Part D in 2006.

This dissertation research will employ a secondary data analysis. The datasets that will be analyzed include the biennial Health and Retirement Study (HRS) Core Survey 2000-2010, the 2000/ 2010 U.S. Census and Neighborhood Change Database (NCDB), and HRS-Medicare linked claims data in 2004 and 2008. Study #1 will use 3-level hierarchical linear models to account for multilevel structure of the data. Study #2 will use the differ-ence-in-differences analytic method to avoid confounding due to non-random assign-ment of study participants in examining a policy effect. The findings of this research are expected to contribute additional knowledge to enhance strategies to reduce or prevent obesity in elderly U.S. adults.



Restricted Data Application Process Timeline

Short ReportUpdated Research PlanData Protection PlanCopy of and Citations for Any Papers

8/15/2014HRS Yearly Recertification

6/30/2014Amendment Request

for Part D Denominator File to

CMS Submitted

CMS Data Missing Beneficiary Summary with Part D due to Data Name Change

2/2/2014 IU IRB

ExpeditedRenewal

Submitted

4/21/2014 CMS Data

Use Agreement Extensions Submitted

11/25/2014Acumen

Data Licensing AgreementRenewal

Form Submitted

Dec Oct Nov Dec2014

Jan Feb Mar Apr May Jun July Aug Sep

2/23IU IRB

Expedited Extended Until

2/23/2016

4/24CMS

Data Use Agreement Extended

Until 4/24/2015

8/7CMS

Amendment/Update Request

Approved

8/25Acumen

Data Licensing Agreement

Updated Form

Approved and Part D

Denominator File

Acquisition

12/4Acumen Data

Licensing Agreement Extended

Until 12/20/2015

4/1/2013 Research Data Assistance

Center (ResDAC ) for CMS Application Submitted

Request Letter for New StudyExecutive SummaryStudy ProtocolData Use AgreementSignature AddendumIRB Evidence of ApprovalEvidence of FundingCost Estimate/InvoiceSpecifications Worksheet

Research PlanRestricted Data Protection PlanCurrent Federal Research GrantCurriculum Vitae Data Order Form

12/10/2012 HRS Application

Submitted

7/1/2013HRS Confidentiality Agreement Submitted

to HRS DDC

Certification of IRBConfidentiality Agreement

3/11/2013 IU IRB

Application Submitted

5/13/2013 HRS

Confidentiality Agreement

Submitted to ORA at IU

8/28/2013 Acumen Formal Contract

Submitted to

ORA at IU

11/1/2013ORA

ApprovedAcumen Formal Contract

Submitted to

MedRIC

Dec Oct Nov Dec2013

Jan Feb Mar Apr MayDec Jun July Aug Sep

1/26HRS

Preliminary Approval

5/10Pre-

liminaryApproval

fromResDAC

4/8IU IRB

Expedited Review

Approval

6/19CMS

Privacy Board Final

Approval

7/1ApprovedSignature

from IU

ORA

8/21HRS DDC

Final Approval

&HRS Data

Acquisition

12/1Acumen

Final Approval

&CMS DataAcquisition



Restricted Data Application Process Timeline (cont.)

Short ReportUpdated Research PlanData Protection PlanCopy of and Citations for Any Papers

8/15/2014HRS Yearly Recertification

6/30/2014Amendment Request

for Part D Denominator File to

CMS Submitted

CMS Data Missing Beneficiary Summary with Part D due to Data Name Change

2/2/2014 IU IRB

ExpeditedRenewal

Submitted

4/21/2014 CMS Data

Use Agreement Extensions Submitted

11/25/2014Acumen

Data Licensing AgreementRenewal

Form Submitted

Dec Oct Nov Dec2014

Jan Feb Mar Apr May Jun July Aug Sep

2/23IU IRB

Expedited Extended Until

2/23/2016

4/24CMS

Data Use Agreement Extended

Until 4/24/2015

8/7CMS

Amendment/Update Request

Approved

8/25Acumen

Data Licensing Agreement

Updated Form

Approved and Part D

Denominator File

Acquisition

12/4Acumen Data

Licensing Agreement Extended

Until 12/20/2015

4/1/2013 Research Data Assistance

Center (ResDAC ) for CMS Application Submitted

Request Letter for New StudyExecutive SummaryStudy ProtocolData Use AgreementSignature AddendumIRB Evidence of ApprovalEvidence of FundingCost Estimate/InvoiceSpecifications Worksheet

Research PlanRestricted Data Protection PlanCurrent Federal Research GrantCurriculum Vitae Data Order Form

12/10/2012 HRS Application

Submitted

7/1/2013HRS Confidentiality Agreement Submitted

to HRS DDC

Certification of IRBConfidentiality Agreement

3/11/2013 IU IRB

Application Submitted

5/13/2013 HRS

Confidentiality Agreement

Submitted to ORA at IU

8/28/2013 Acumen Formal Contract

Submitted to

ORA at IU

11/1/2013ORA

ApprovedAcumen Formal Contract

Submitted to

MedRIC

Dec Oct Nov Dec2013

Jan Feb Mar Apr MayDec Jun July Aug Sep

1/26HRS

Preliminary Approval

5/10Pre-

liminaryApproval

fromResDAC

4/8IU IRB

Expedited Review

Approval

6/19CMS

Privacy Board Final

Approval

7/1ApprovedSignature

from IU

ORA

8/21HRS DDC

Final Approval

&HRS Data

Acquisition

12/1Acumen

Final Approval

&CMS DataAcquisition


Presentations/Publications



Chen, C.C., Seo, D.C., & Lin, H.C. (In Preparation). Longitudinal state-level socioeco-nomic and environmental effects on change in body mass index among U.S. middle-aged and older adults: A three-level growth model

Chen, C.C., Lin, H.C., & Seo, D.C. (In Preparation). Medicare Part D implementation and associated health impact among older adults

Chen, C.C., Seo, D.C., & Lin, H.C. (2014, November). Impact of gender, individual/neigh-borhood socioeconomic status (SES), and race/ethnicity on body mass index trajectories among the U.S. middle-aged and older adults. American Public Health Association, New Orleans, LA.

Abstract:

Introduction: Obesity has become epidemic in the U.S. while adult men and women showed different obesity prevalence growth rates (29% and 7.2% respectively) in 2000-2010. As the aging population grows, obesity and related chronic diseases will increase costs of social welfare programs like Medicare. Literature is inconsistent on the direc-tions/independence of associations between individuals’ and neighborhood sociodemo-graphic characteristics and obesity.

Methods: The datasets including Health and Retirement Study respondents who had lived in the same Census tract in 2000-2010 (N=6,261) and 2000/2010 U.S. Census were analyzed. Three-level hierarchical growth model with individual and state variations was fit to model BMI trajectories using HLM 7 based on the aggregated census tract level demographic variables.

Results: BMI has a significant increase over time following a quadratic decelerating trajectory (p < 0.001). Relative to women, men have higher BMI at baseline (b = 0.43, p = 0.001), and have higher growth rate (b = 0.024, p < 0.001). Non-Hispanic White people have significantly lower initial BMI (b = -1.48, p < 0.001); however, the growth rate is not significantly different from the other race/ethnicity groups. Neighborhood SES has im-pact on BMI at baseline (b = 0.25, p < 0.01) with no difference of the growth rates among states. The cross-level interaction between neighborhood SES and personal race/ethnicity is not significant while individual variables have stronger main effects.

Discussion: The individuals’ race/ethnicity and neighborhood SES have independent influence on BMI trajectories. The interventions may need to target on middle-aged and older male individuals.



YoussefAgha, A., Chen, C.C., Labib, N., Lohrmann, D., Salama, S., & ElSherbibi, N. (2014) A comparison of the Egyptian public’s rankings of key public health and envi-ronmental issues by occupation and by gender. In: A.H. YoussefAgha, S.E. Obeng, W.P. Jayawardene (Eds.), Contemporary issues in public health in North Africa and Middle East (pp.87-108). New York, NY: Nova Science.

Chen, C.C. (2014, May) Introduction to GIS & a rural home healthcare needs project. To-bacco, Obesity, and Behavioral Wellness Research Working Group, Bloomington, IN.

Sadler, K., Seitz-de-Martinez, B., Agley, D., Chen, C.C., & Anderson, K.S. (2014). Blue river services feasibility study for home health care. Bloomington, IN: Indiana Prevention Resource Center.

Chen, C.C., & Zhao, X.Y. (2012, April) Effect of statewide smoking bans in public plac-es: Implications from BRFSS smoking related questions. Health Policy Working Group, Bloomington, IN.

Chen, C.C. (Ed.). (1999). Road to the U.S. academia: Effective ways to become a successful scholar. Taipei, Taiwan: The Foundation for Scholarly Exchange in Taiwan.

Chen, C.C. (1999). Sport injury prevention and emergency care. Taipei, Taiwan: Chinese Professional Baseball League.


GIS Portfolio

GIS Portfolio


The purpose of this project is to use nationally representative data and metropolitan sta-tistical area (MSA) as the analytic unit to investigate the relationship among walkability, physical activity, food outlet density, expenditure of fast food, and trajectories of BMIs.

Current Project

Illustrations of an ongoing project regarding sample selection process based on MSAs and density analysis of fast food restaurants. Model builder will be used for more in-depth analyses.

GIS Portfolio


This study was conducted to provide information for nonprofit organization in Harrison County in regards to home health care availability, gaps in service, and related needs for services. This is a project subject to the lifespan of all Harrison County residents that are homebound, have limited mobility, limited available transportation, and those living with high levels of medical care.

Rural Home Healthcare Need Analysis Project

Selected figures showing results of geostatistics, net-work analysis on rural transportation services, and side-by-side simultaneous illustration of block groups and zip codes for project clients

GIS Portfolio


Rural Home Healthcare Need Analysis Project (cont.)

GIS Portfolio


This project was S603 course final assignment. The main purpose was to investigate the distribution of the Fast Food restaurants and schools from a local entity (e.g., Blooming-ton/Monroe County) in Indiana to provide preliminary information for further research opportunities.

Obesity & Fast Food Restaurants Project

Application of geoc-oding and various spatial analyses on Indiana health indicators, obesity determinants at local level in Blooming-ton, Indiana

GIS Portfolio


Obesity & Fast Food Restaurants Project (cont.)


Proficiency

Proficiency


Statistics Analyses:

• Multilevel Modeling • Multivariate Statistics • Structural Equation • Modeling; Survival Analyses • Spatial Analyses

Skills

• Geostatistics • Georeference • Geocoding • Geodatabase • Model Builder • Cartographic design

Statistics Packages:

• STATA • HLM • SPSS • SAS

Programming/Software

• ArcGIS 10.x • QGIS • Adobe Creative Cloud • Microsoft Office • HTML & CSS • Basic knowledge of Javascript

Statistics

GIS

Training

• Courses/Workshops » 8-Week Instructor-Led Training Course (1.5 Credits), Indiana University,

Bloomington, IN » Certificate of Completion, ArcGIS Desktop Workshop: Getting Started with

GIS (3 contact hours), Indiana University-Purdue University, Indianapolis, IN

» Certificate of Completion, ArcGIS Desktop Workshop: Geocoding & Spatial Analyses (3 contact hours), Indiana University, Bloomington, IN

• ESRI Courses: Learning ArcGIS Desktop; Getting Started with GIS; Learning ArcGIS Spatial Analyst; Turning Data into Information Using ArcGIS 9; Geopro-cessing with ArcGIS Desktop; Understanding Geographic Data; Creating, Edit-ing, and Managing Geodatabases for ArcGIS Desktop; Python Scripting for Map Automation in ArcGIS 10

• Lynda Training Courses: » Certificate of Completion, Up and Running with ArcGIS with Adam Wilbert

(3 hours) » Certificate of Completion, Real-World GIS with Jason VanHorn(3 hours) » Certificate of Completion, GIS on the Web with Jason VanHorn(2 hours)

ResearchPortfolioCheng-Chia B. Chen

PhD Candidate

Department of Applied Health ScienceSchool of Public Health

Indiana University Bloomington

Email: [email protected]: 812 250 6658

Please visit my multimedia sitehttp://pages.iu.edu/~brichen/

Created by Cheng-Chia “Brian” ChenVersion 1.0

http://pages.iu.edu/~brichen

Documents

Cheng-Chia 'Brian' Chen - Research Portfoliocchen236/pdf/Research_Portfolio.pdf · Chen, Research Portfolio 5 people who walk to work) are associated with longitudinal trajectory