A Patient-Centered PaTH to Addressing Diabetes: Impact of State Health Policies on Diabetes Outcomes and Disparities and Enhancement Study: Telemedicine Access and Severe Disease Outcomes in Patients With Diabetes and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)
Conditions: Obesity, Diabetes, Covid19
Sponsor: Milton S. Hershey Medical Center

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HRP-596 - Protocol for Human Subject Research - 
Chart Review and/or Analysis of Existing Restricted Data Set Study 
 
Protocol Title: 
A Patient-Centered PaTH to Addressing Diabetes: Impact of State Health Policies on Diabetes Outcomes and Disparities 
 
Principal Investigator: 
Name:  Jennifer Kraschnewski 
Department:  Medicine and Public Health Sciences 
Telephone: 717 531 8161 
E-mail Address: jkraschnewski@hmc.psu.edu 
 
Version Date: 
3/28/2016 
 
Important Instructions for Using This Protocol Template: 
1. 
Add this completed protocol template to your study in CATS IRB (http://irb.psu.edu) on the “Basic Information” 
page, item 7.  
2. 
This template is provided to help investigators prepare a protocol that includes the information necessary for the 
IRB to determine whether a study meets all criteria for approval for research involving chart reviews or the use of 
existing restricted data sets only.  Before choosing this protocol, please review information regarding the definition 
of Human Research available in several places, including the Investigator Manual and HRP-310-Human Research 
Determination, to ensure that use of this protocol template is appropriate.  If your study is not Human Research 
you may use HRP-594- Protocol for Not Human Subjects Research Determination to receive an official 
determination from the IRB that your study is not human research.  
3. 
Type your protocol responses below the gray instructional boxes of guidance language.  If the section or item is 
not applicable, indicate not applicable. 
4. 
For research being conducted at Penn State Hershey or by Penn State Hershey researchers only, delete the 
instructional boxes from the final version of the protocol prior to upload to CATS IRB (http://irb.psu.edu).  For all 
other research, do not delete the instructional boxes from the final version of the protocol. 
5. 
When making revisions to this protocol as requested by the IRB, please follow the instructions outlined in the Study 
Submission Guide available in the Help Center in CATS IRB (http://irb.psu.edu) for using track changes.  
 
If you need help… 
University Park and other campuses: 
Office for Research Protections Human Research Protection Program 
The 330 Building, Suite 205 
University Park, PA 16802-7014 
Phone: 814-865-1775 
Fax: 814-863-8699 
Email: irb-orp@psu.edu 
College of Medicine and Hershey Medical Center: 
Human Subjects Protection Office 
90 Hope Drive, Mail Code A115, P.O. Box 855 
Hershey, PA 17033 
(Physical Office Location: Academic Support Building Room 1140) 
Phone: 717-531-5687 
Fax number: 717-531-3937 
Email: irb-hspo@psu.edu 
 
 

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Table of Contents 
1.0 
Objectives 
2.0 
Background 
3.0 
Inclusion and Exclusion Criteria 
4.0 
Sample Selection 
5.0 
Consent Process and Documentation 
6.0 
HIPAA Research Authorization and/or Waiver or Alteration of Authorization 
7.0 
Study Design and Procedures 
8.0 
Statistical Plan 
9.0 
Confidentiality, Privacy and Data Management 
10.0 
Risks 
11.0 
Potential Benefits to Subjects and Others 
12.0 
Other Approvals 
13.0 
Multi-Site Research 
14.0 
Adverse Event Reporting 
15.0 
Study Monitoring, Auditing and Inspecting 
16.0 
Future Undetermined Research: Data Banking 
17.0 
References 
 
 

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1.0 
Objectives 
 
The overarching goal of this proposal is to understand the comparative effectiveness of obesity counseling as covered 
by Centers for Medicare and Medicaid Services (CMS) in improving weight loss for adults either with or at high risk of 
type 2 diabetes. CMS and most insurers now include obesity screening and counseling benefits, with no cost sharing to 
patients.1 Since overweight patients are at highest risk for diabetes, improved weight management services could 
prevent diabetes and its negative health outcomes. Beneficiaries with obesity are eligible for up to 20 face-to-face visits 
for weight counseling in the primary care setting. We propose comparing weight and diabetes outcomes in three states 
using electronic health record (HER) and claims data before and after this policy was implemented. The PaTH network 
brings together six academic health systems – the University of Pittsburgh/University of Pittsburgh Medical Center, Penn 
State College of Medicine/Hershey Medical Center, Temple University School of Medicine/Temple Health, and the Johns 
Hopkins University/Johns Hopkins Health System/Johns Hopkins Health Care, Geisinger Health Systems, and The 
University of Utah – to function as an integrated research network and learning health system, providing an 
infrastructure for observational studies, such as the one proposed here, that require populations beyond a single health 
system to answer important patient-centered health services questions (www.pathnetwork.org). Using the PaTH Clinical 
Data Research Network (CDRN) infrastructure, we propose the following specific aims:  
 
Aim 1: Evaluate the impact of universal preventive service coverage for obesity screening and counseling on weight 
loss, diabetes incidence, and diabetes outcomes, in patients with diabetes or at high risk for diabetes (defined by 
body mass index (BMI) ≥ 25). We will determine how the annual probability of receiving obesity and/or nutritional 
counseling (as defined by Current Procedural Terminology (CPT) code) changed pre- and post-policy across all insurers in 
a cohort of patients with diabetes and at high risk for diabetes. We hypothesize that individual patients are more likely 
to receive counseling following coverage implementation. Further, we hypothesize that patients who receive a greater 
number of face-to-face visits will have greater weight loss compared to those who receive fewer visits.  
 
Aim 2: Compare patient weight loss and diabetes-related outcomes among those who receive obesity screening and 
counseling to those who do not, following implementation of preventive service coverage. We will examine post-
policy impact of obesity screening and counseling in a cohort of patients with diabetes and at high risk for diabetes. 
Specific outcomes to be examined include weight loss, diabetes incidence, and diabetes outcomes (including 
hemoglobin A1c, controlled blood pressure, use of a statin medication). Further, we will determine patient 
characteristics, including demographics (age, race/ethnicity, rurality), and practice characteristics, including provider 
type, and their impact on receiving/providing obesity screening and counseling. Understanding patient and practice 
characteristics most likely to engage in obesity counseling can identify best practices and inform how to increase 
engagement by both patients and providers. 
 
2.0 
Background  
 
2.1 
Scientific Background and Gaps 
 
Overweight and obesity are America’s number one health concern. The prevalence of obesity in the US is greater than 
36%,1 which is far above the Healthy People 2020 objective of less than 30.5%.2 Perhaps most concerning is the rate that 
obesity has increased, having doubled since 1970.3 As the second most preventable cause of death,4 obesity is a risk 
factor for diabetes, cardiovascular disease, stroke, and cancer, all major causes of death in the US.5 Addressing obesity 
through lifestyle interventions decreases the risk of developing type 2 diabetes, a disease which affects over 29 million 
people (9.3% of the US population).6 Diabetes is associated with serious complications, including cardiovascular disease, 
blindness, renal failure and lower extremity amputation. Although complications are preventable with proper medical 
and lifestyle management, including weight loss, nearly half of patients with diabetes do not have adequate glycemic 
control.7  
 

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Primary care clinics may be an ideal setting for weight control interventions. Greater than 80% of Americans see a PCP 
regularly and access to primary care is expected to increase with health care reform.8,9 Further, as PCPs identify and 
treat the multitude of conditions affected by being overweight, including diabetes, they are ideally positioned to best 
engage their patients in weight management. The Centers for Medicare and Medicaid Services (CMS) implemented a 
healthcare procedure coding system code for intensive behavioral therapy (IBT) for obesity within primary care settings 
in 2012 to facilitate payment for addressing obesity, which was followed by universal coverage by insurers for IBT for 
adults of all ages in 2013.10 However, the impact of this coverage on patient-centered outcomes is largely unknown.   
 
The proposed work leverages the novel infrastructure of the Patient-Centered Outcomes Research Institute-funded 
PaTH Clinical Data Research Network (CDRN), a partnership of four Mid-Atlantic academic health systems (Penn State 
Hershey Medical Center, University of Pittsburgh Medical Center, Temple Health System, and Johns Hopkins Health 
System) that have established governance to operate as an integrated research network. In 2015, the University of Utah 
and Geisinger Health System also joined PaTH, creating an electronic health record (EHR)-based data infrastructure 
across three states (Maryland, Pennsylvania, and Utah). 
 
We have additionally added a focus on rural/urban differences in provision of obesity screening and counseling and the 
resultant impact on weight loss and diabetes incidence. Rural populations have a 17% higher prevalence of diabetes 
when compared to their urban counterparts.11,12 As a result, diabetes is the second highest priority for Rural Healthy 
People 2020.13 Additionally, rural populations have lower rates of guideline-concordant diabetes services, although this 
has been inadequately investigated.14 Studies report adults with diabetes living in rural communities are less likely to 
receive adequate diabetes-related care than non-rural residents, but are limited in study design (i.e., reliant on patient 
self-report or medical expenditure data).11,15 We propose leveraging the novel infrastructure of the Patient-Centered 
Outcomes Research Institute-funded PaTH CDRN to understand the rural and urban effects on diabetes and its 
complications. The integration of six large health institutions spanning a diversity of rural and urban populations has the 
unique opportunity to overcome limitations of prior studies by using health record data.14 In this way, our proposed 
study will address important gaps in our understanding of rural/urban differences in diabetes outcomes and guide 
future approaches to address and eliminate disparities. 
 
Utilizing the PaTH CDRN offers an opportunity to study rural/urban effects, given that the 6 health institution network 
(University of Pittsburgh Medical Center, Penn State Hershey Medical Center, Temple Health System, Johns Hopkins 
Health System, University of Utah Health Care, and Geisinger Health System) covers significant populations in both rural 
and urban counties. For example, PaTH CDRN’s service area includes more than 85% of the population of Pennsylvania, 
including coverage in nearly every one of Pennsylvania’s 67 
counties (48 rural and 19 urban, see map below). Further, 
27% of Pennsylvania’s residents live in rural counties, which 
is higher than the national average of 20%, offering 
opportunity to better understand diabetes care in this 
setting.13 The addition of the University of Utah also offers a 
different geographic location with similar opportunities to 
study rural/urban effects. This system provides care for 
Utahns and residents of five surrounding states in a referral 
area encompassing more than 10% of the continental US, 
the vast majority of which is rural. Johns Hopkins Health 
System, Temple Health System, and the University of 
Pittsburgh Medical Center offer three different urban 
populations for comparison. 
 
2.2 
Previous Data 
 
Preliminary Studies 
 
PaTH Clinical Data Research Network 

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Overview.  The PaTH Clinical Data Research Network (CDRN) brings together four Mid-Atlantic academic health 
systems—the University of Pittsburgh/University of Pittsburgh Medical Center (UPMC), Penn State College of 
Medicine/Hershey Medical Center, Temple University School of Medicine/Temple Health, and the Johns Hopkins 
University/Johns Hopkins Health System/Johns Hopkins Health Care to function as an integrated research network and 
learning health system, providing an infrastructure for pragmatic clinical trials and observational studies that require 
populations beyond a single health system to answer important patient-centered clinical and health services questions 
(www.pathnetwork.org). Funded by the Patient-Centered Outcomes Research Institute (PCORI) in March 2014, the PaTH 
CDRN is one of 11 CDRNs across the country. Along with 18 Patient-Powered Research Networks (PPRNs), these 11 
CDRNs form PCORnet—the National Patient-Centered Clinical Research Network—a national network for conducting 
clinical outcomes research (www.pcornet.org). The goal of PCORnet is to improve the nation’s capacity to conduct 
comparative effectiveness research by a creating large, highly representative network from which to draw data, while 
protecting patient privacy and ensuring data security.  
 
PaTH’s mission during its initial funding period is to create the technical, governance, and patient- and clinician-
engagement infrastructure to conduct clinical and health services research across diverse health systems.42 For this 
proposal, we will include all four of the PaTH institutions (Pittsburgh, Penn State, Johns Hopkins, and Temple), thus 
representing patients from Pennsylvania and Maryland. The University of Utah will join the PaTH network in 2015, which 
is an exciting new partnership for our network (see Table 3). The addition of the University of Utah greatly strengthens 
the proposed project by adding a state that has not adopted Medicaid expansion.  
 
The University of Pittsburgh is the lead site for the PaTH Network, with Rachel Hess, MD, MS as the PaTH Network PI.  
The overall operations of the PaTH Network are run by the PaTH Steering under Dr. Hess’ lead. Dr. Hess and the PaTH 
Steering Committee enthusiastically support this Penn State application to RFA-DP-15-001 (see Dr. Hess’ letter of 
support), which will leverage the data infrastructure, established governance and regulatory policies related to data 
security, data sharing and human subjects protection of the PaTH Network. 
 
Table 3.  Population Overview at Proposed Clinical Sites 
 
 
Penn State 
Hershey 
Pitt/UPMC 
TUHS 
Hopkins 
University of 
Utah 
EHR platform 
Cerner 
Cerner 
(inpatient) 
Epic (outpatient) 
 
Epic 
Epic 
Epic 
Distinct patients with at least one 
encounter or record in EHR 
615,012 
5,537,583 
457,388 
4,800,000 
1,602,245 
Active patients with data in EHR 
520,310 
1,880,457 
323,682 
1,764,221 
581,568 
 
 
PaTH Patient Population.  The patients in the PaTH network are diverse—22% are aged 17 years or younger and 
20% are aged 65 years or older. Over 25% are non-white and 20% have public insurance (excluding Medicare) or no 
insurance. The organizations are also diverse with many affiliated community-based hospitals and outpatient practices 
in addition to their academic hospitals. Other facilities include rehabilitation hospitals, dialysis centers, fitness and 
wellness centers, psychiatric hospitals, ambulatory surgery centers, and home healthcare support.  
 
PaTH Data Sources. PaTH leverages health-related data from: 1) electronic health records (EHR), 2) patient-
reported outcomes (PROs), 3) insurance claims data, and 4) biospecimen data. The PaTH data that will be used in the 
proposed work will be limited to EHR data and claims data.  
 
 
PaTH Governance and Regulatory Issues.  A significant challenge of multi-institutional research networks is 
managing the governance and regulatory issues that arise when different institutions have their own procedures for 
conducting research and managing their clinical health records. Perhaps the largest tasks to date for the PaTH CDRN 
have been the establishment of several governance policies that were required in order for the network to function. 

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Thus, the proposed research not only builds on the PaTH data infrastructure, but also on its established governance and 
regulatory policies. The most important agreements that have been executed are: 1) the PaTH network’s data use 
agreement (DUA) and the 2) PaTH network’s reliance agreement for a central Institutional Review Board (IRB) to 
oversee the protection of human subjects. The PaTH DUA establishes a secure environment for sharing and handling 
patient data for the purposes of research within the PaTH network. These policies describe data standardization, data 
privacy standards, and sharing of data within the CDRN.  Under the PaTH DUA, we will be able to conduct the research 
described in this proposal without delays due to need to establish such agreements. 
 
The PaTH network has also established a centralized process for IRB reviews. Creating separate IRB protocols with 
different formats and procedures to be reviewed by separate IRBs would be an inefficient and ineffective process. This 
problem has been recognized by the National Institutes of Health (NIH), who is promoting the use of a single IRB in 
multi-site clinical research studies to reduce duplication of effort, speed-up the initiation of important research, and 
save time and resources.43 To this end, the PaTH network has established a reliance agreement naming Johns Hopkins 
IRB as our central IRB of record. Under the reliance agreement, the other institutions agree to allow the Johns Hopkins 
IRB to review the study protocol, and to honor the approval of the protocol. To ensure that each PaTH institution would 
have input into the review process, we convened the PaTH Network Protocol Review Committee, or PNPRC. Two IRB 
members from each institution serve on the PNPRC, an IRB member and a community member, currently totaling 8 
members. Only after the PNPRC approves a PaTH protocol, does it then get submitted to the Johns Hopkins IRB for 
centralized review.   
 
2.3 
Study Rationale 
 
The proposed project is significant for several reasons. First, diabetes is a leading public health concern and associated 
with significant economic burden. Recent health policies changes (e.g.,  CMS coverage) are expected to impact diabetes 
and pre-diabetes outcomes and the proposed project will capture differences in these outcomes through a broad region. 
Understanding the uptake of the CMS weight counseling benefit and its effect on diabetes and pre-diabetes outcomes 
can inform future policies to improve overall care for patients.  
 
3.0 
Inclusion and Exclusion Criteria 
 
3.1 
Inclusion Criteria 
 
Patients with Diabetes 
 
Ages 18 and older 
 
Indication of Type 2 Diabetes as defined using a clinically validated algorithm:  type 2 diabetes 
mellitus on the problem list, diabetes-specific medications, hemoglobin A1c (HbA1c) results > 
7.0%, or one inpatient diagnosis code or two out-patient diagnosis codes for type 2 diabetes 
(ICD-9 codes 250.xx) 
 
patients who have either: (1) had at least 2 outpatient primary care visits in one of the PaTH 
health systems in the past 3 years (since January 1, 2012), or (2) for whom claims data are 
available 
 
Patients with Pre-Diabetes (At risk): 
 
Ages 18 and older 
 
BMI > 25 kg/m2  
 
patients who have either: (1) had at least 2 outpatient primary care visits in one of the PaTH 
health systems in the past 3 years (since January 1, 2012), or (2) for whom claims data are 
available 
 
3.2 
Exclusion Criteria 
 
Patients under the age of 18 

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4.0 
Sample Selection 
 
4.1 
Source of the charts/existing restricted data set 
The proposed project will utilize the PaTH Network’s existing infrastructure for sharing EHR data across our network. The 
PaTH Network population represents real-world populations of patients, reducing the likelihood of selection biases that 
occur with clinical trial data or data from individuals within the same insurance plan. This, in turn, enhances the 
generalizability of our results. While research records residing at each site will retain identifying information, research 
records will be de-identified (i.e., personal health information (PHI) will be removed from individual records) prior to 
making data from the local sites available to Penn State. 
 
PaTH Patient Population.  The patients in the PaTH network are diverse—22% are aged 17 years or younger and 20% are 
aged 65 years or older. Over 25% are non-white and 20% have public insurance (excluding Medicare) or no insurance. 
The organizations are also diverse with many affiliated community-based hospitals and outpatient practices in addition 
to their academic hospitals. Other facilities include rehabilitation hospitals, dialysis centers, fitness and wellness centers, 
psychiatric hospitals, ambulatory surgery centers, and home healthcare support.  
 
PaTH Data Sources. For this study, we will leverage PaTH’s : 1) electronic health records (EHR), 2) patient-reported 
outcomes (PROs), and 3) insurance claims data.  
 
4.2 
Identification of subjects/charts/existing data 
The University of Pittsburgh is the lead site for the PaTH Network.  The overall operations of the PaTH Network are run 
by the PaTH Steering Committee, which will leverage the data infrastructure, established governance and regulatory 
policies related to data security, data sharing and human subjects protection of the PaTH Network. 
 
We will use the PaTH Network to identify potential patients who meet our eligibility criteria. PaTH’s infrastructure 
enables and allows for the development, validation, and deployment of electronic algorithms for identifying patients 
with the conditions of interest. PaTH will continue to follow the guidance of the NIH Health Systems Collaboratory’s 
work on EHR-based phenotyping to create computable phenotypes for the proposed diabetes and pre-diabetes cohorts.  
Phenotypes will be based on the above inclusion/exclusion criteria. 
 
5.0 
Consent Process and Documentation  
 
5.1 
Consent Process  
 
Check all that apply: 
   
Informed consent will not be obtained – request to completely waive or alter the informed 
consent requirement. [Complete Section 5.1.1] 
[Review the “CHECKLIST: Waiver or Alteration of Consent Process (HRP-410)” to ensure you have 
provided sufficient information for the IRB to make these determinations. HRP-410 can be 
accessed by clicking the IRB Library link in CATS IRB (http://irb.psu.edu). Waiver of consent is the 
most frequently requested type of consent for both retrospective and prospective chart reviews 
and existing restricted data sets.]   
 
  
Informed consent will be sought and documented with a written consent form [Complete 
Sections 5.1.2, 5.2.1 and 5.3 (when 5.3 is applicable)] 
[Review “SOP: Written Documentation of Consent (HRP-091)” for information about the process 
to document the informed consent process in writing.  HRP-091 can be accessed by clicking the 
IRB Library link in CATS IRB (http://irb.psu.edu).] 
 

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Implied or verbal consent will be obtained – subjects will not sign a consent form [Complete 
Sections 5.1.2, 5.2.2 and 5.3 (when 5.3 is applicable)] 
[Review “CHECKLIST: Waiver of Written Documentation of Consent (HRP-411)” to ensure that 
you have provided sufficient information. HRP-411 can be accessed by clicking the IRB Library 
link in CATS IRB (http://irb.psu.edu). If your research presents no more than minimal risk of harm 
to subjects and involves no procedures for which written documentation of consent is normally 
required outside of the research context, the IRB will generally waive the requirement to obtain 
written documentation of consent.] 
 
5.1.1 
Waiver or alteration of the informed consent requirement 
 
The research poses no more than minimal harm to subject (i.e., loss of confidentiality/privacy). The 
waiver will not adversely affect the rights and welfare of subjects because the research results will not 
affect the care of the subjects. Given the number of subjects and the possibility that subjects may no 
longer be living or lost to follow-up, this research would not be practical without this waiver. No 
information which would be pertinent to the on-going care of individual subjects is expected to result 
from this study. 
 
5.1.2 
Obtaining Informed Consent 
N/A 
 
 
5.2 
Consent Documentation 
 
5.2.1 
Written Documentation of Consent 
N/A 
 
5.2.2 
Waiver of Documentation of Consent (Implied consent, Verbal consent, etc.) 
 
The proposed research presents no more than minimal risk of harm to subjects and involves no 
procedures for which written documentation of consent is normally required outside of the research 
context. 
 
5.3 
Consent – Other Considerations  
 
5.3.1 
Non-English Speaking Subjects 
N/A 
 
5.3.2 
Cognitively Impaired Adults 
N/A 
 
5.3.2.1 Capability of Providing Consent 
N/A 
5.3.2.2 
Adults Unable To Consent 
N/A 
5.3.2.3 Assent of Adults Unable to Consent 
N/A 
 
5.3.3 
Subjects who are not yet adults (infants, children, teenagers)  
 
5.3.3.1 
Parental Permission 
 
         N/A 

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5.3.3.2 Assent of subjects who are not yet adults 
N/A 
 
6.0 
HIPAA Research Authorization and/or Waiver or Alteration of Authorization 
 
6.1 
Authorization and/or Waiver or Alteration of Authorization for the Uses and Disclosures of PHI 
 
Check all that apply: 
  
Not applicable, no identifiable protected health information (PHI) is accessed, used or 
disclosed in this study. [Mark all parts of sections 6.2 and 6.3 as not applicable] 
 
 
Authorization will be obtained and documented as part of the consent process. [If this is the 
only box checked, mark all parts of sections 6.2 and 6.3 as not applicable] 
 
 
Partial waiver is requested for recruitment purposes only (Check this box if patients’ medical 
records will be accessed to determine eligibility before consent/authorization has been 
obtained).  [Complete all parts of sections 6.2 and 6.3] 
 
 
Full waiver is requested for entire research study (e.g., medical record review studies).  
[Complete all parts of sections 6.2 and 6.3]  
 
 
Alteration is requested to waive requirement for written documentation of authorization 
(verbal authorization will be obtained). [Complete all parts of sections 6.2 and 6.3] 
 
6.2 
Waiver or Alteration of Authorization for the Uses and Disclosures of PHI 
 
6.2.1 
Access, use or disclosure of PHI representing no more than a minimal risk to the privacy of the 
individual 
 
6.2.1.1 Plan to protect PHI from improper use or disclosure 
 
Information is included in the “Confidentiality, Privacy and Data Management” 
section of this protocol. 
 
6.2.1.2 Plan to destroy identifiers or a justification for retaining identifiers  
 
Data will be de-identified. 
 
6.2.2 
Explanation for why the research could not be practicably conducted without access to and 
use of PHI 
 
Retrieval of study data necessitates access to patient charts through their MRNs.  Once this 
information is obtained, it will be identified with code numbers linked to patient MRN’s 
 
6.2.3 
Explanation for why the research could not practicably be conducted without the waiver or 
alteration of authorization 
Given the number of subjects and the possibility that subjects may no longer be living or have 
current contact information, this research would not be practical without waiver. No clinical 
information which would be pertinent to the care of individual subjects is expected to result 
from this study 
 
6.2.4 
Waiver or alteration of authorization statements of agreement 

Page 10 of 20 (V.10/27/2015)  
 
Protected health information obtained as part of this research will not be reused or disclosed to 
any other person or entity, except as required by law, for authorized oversight of the research 
study, or for other permitted uses and disclosures according to federal regulations. The research 
team will collect only information essential to the study and in accord with the ‘Minimum 
Necessary’ standard (information reasonably necessary to accomplish the objectives of the 
research) per federal regulations. Access to the information will be limited, to the greatest extent 
possible, within the research team. All disclosures or releases of identifiable information granted 
under this waiver will be accounted for and documented. 
 
7.0 
Study Design and Procedures 
 
7.1 
Study Design 
Check all that apply: 
 
Analysis of an existing restricted data set.  Existing means that all of the data to be used in this 
study are already in existence when the study is initially submitted to this IRB for review.  
 
 
Retrospective chart/record review – A retrospective chart/record review study means that all 
of the data to be used in this study are already in existence when the project is initially 
submitted to this IRB for review.*    
*Provide date range of chart/record review (mm/dd/yyyy to mm/dd/yyyy):  
2009-2019 
 
 
Prospective chart/record review – A prospective chart/record review study means that all of 
the data are not in existence when the project is initially submitted to this IRB for review. 
 
7.2 
Study Procedures 
 
For the specific needs of this proposal, we will define 2 patient cohorts using the PaTH Network:  (1) Diabetes cohort and 
(2) At-risk for diabetes cohort. As demonstrated in the table below, there are over 328,000 patients with a diagnosis of 
diabetes (defined as age 18 and older with a diagnosis of diabetes mellitus – ICD9 250.xx) and over 2 million patients at 
risk for diabetes (defined as age 18 and older with a BMI of ≥ 25) within the PaTH network.  
 

Page 11 of 20 (V.10/27/2015)  
Diabetes Cohort Definition. During year 1 of the proposed project, the investigative team, in collaboration with the PaTH 
Network, will identify a valid cohort of patients with type 2 diabetes. The cohort of patients under study will be defined 
as all patients age 18 and older with an indication of type 2 diabetes during the proposed study time frame. Patients will 
be classified as having diabetes using a clinically validated algorithm:  type 2 diabetes mellitus on the problem list, 
diabetes-specific medications, hemoglobin A1c (HbA1c) results > 7.0%, or one inpatient diagnosis code or two out-
patient diagnosis codes for type 2 diabetes (ICD-9 codes 250.xx). The diabetes cohort will be further limited to patients 
who will likely be captured in the PaTH EHR records or claims data so that outcome assessments can occur. Thus, we will 
further limit the diabetes cohort to patients who have either: (1) had at least 2 outpatient primary care visits in one of 
the PaTH health systems in the past 3 years (since January 1, 2012), or (2) for whom claims data are available. The 
cohort will be dynamic, with new patients added into the cohort after 2015 as they meet the diabetes cohort definition 
prospectively. The observational period for the outcome variables will be for the 10-year period from 2009-2019, thus 
including 3 years of data prior to the first policy change (CMS instituting coverage for intensive behavioral therapy for 
obesity) and 3 years after the last policy change (Pennsylvania Medicaid expansion) under study. 
 
Following the definition of the diabetes cohort and key diabetes outcomes and covariates as described above, an initial 
extraction of variables will be conducted in Year 2 of the proposed project for years 2009-2015. This early data 
extraction from the PaTH Network will allow for cohort validation and data cleaning and editing, as well as required 
programming and determination of the analysis models. We will utilize this initial data extraction in Years 2 and 3 to 
analyze the impact of early health policy changes, as well as to prepare a manuscript of both the protocol and early 
findings of policy impact. The final data extraction will occur during the final quarter of Year 4 of the proposed project, 
allowing for completion of a ten-year time period (2019).  
 
At-risk Cohort Definition. The cohort of patients under study will be defined as patients age 18 and older who are at risk 
for the development of diabetes, based on being overweight. Patients seen at one of the four PaTH institutions will be 
included in the at-risk cohort if they have a BMI ≥ 25 kg/m2, based on most recent recorded weight and at least one 
recorded height. The at-risk cohort will be further limited to patients who will likely to be captured in the PaTH EHR 
records or claims data so that outcome assessments can occur. Thus, we will further limit the at-risk cohort to patients 
who have either: (1) had at least 2 outpatient primary care visits in one of the PaTH health systems in the past 3 years 
(since January 1, 2012), or (2) for whom claims data is available. The cohort will be dynamic, with new patients added 
into the cohort after 2015 as they meet the at-risk cohort definition prospectively. Patients will not be removed from the 
cohort, even if they are no longer overweight. The observational period for the outcome variables will be for the 10-year 
period from 2009-2019, thus including 3 years of data prior to the first policy change (CMS instituting coverage for 
Patient 
Characteristics 
Penn State 
University 
(N=25,219) 
University of 
Pittsburgh 
(N=150,589) 
Johns 
Hopkins 
University 
(N=60,324) 
Temple 
University 
(N=40,536) 
University of 
Utah 
(N=51,787) 
PaTH Total 
(N=328,455) 
Patients with Diabetes 
Insurance type 
   Private 
   Medicaid 
   Medicare 
   Uninsured 
 
9,874 (38%) 
1,890 (8%) 
12,998 (52%) 
457 (2%) 
 
60,223 (40%) 
10,165 (7%) 
54,675 (36%) 
11,625 (8%) 
 
32,371 (54%) 
727 (1%) 
22,826 (38%) 
485 (1%) 
 
5,261 (13%) 
21,759 (54%) 
10,916 (27%) 
2,599 (6%) 
 
21,264 (41%) 
4,189 (8%) 
23,997 (46%) 
2,337 (5%) 
 
128,993 (39%) 
38,730 (12%) 
125,412 (38%) 
17,503 (5%) 
Race 
   White 
   African-American 
   Other 
 
21,780 (86%) 
1,679 (7%) 
1,760 (7%) 
 
116,056 (77%) 
15,336 (10%) 
19,197 (13%) 
 
32,544 (54%) 
20,053 (33%) 
7,727 (13%) 
 
14,347 (35%) 
14,656 (36%) 
11,533 (29%) 
 
39,580 (76%) 
944 (2%) 
11,263 (22%) 
 
224,307 (68%) 
52,668 (16%) 
51,480 (16%)  
 
Hispanic Ethnicity 
1,054 (4%) 
697 (0.5%) 
2,002 (3%) 
7,356(18%) 
6,077 (12%) 
17,186 (5%) 
Female Gender 
12,014 (48%) 
70,623 (47%) 
30,912 (51%) 
22,485 (55%) 
25,727 (50%) 
161,761 (49%) 
Patients At-Risk for Diabetes 
 
 (N=167,799) 
(N=950,020) 
 (N=471,860) 
 (N=212,314) 
 (N=260,506) 
(N=2,062,499) 
BMI 
  25-29.9 
  30-34.9 
  35-39.9 
  40+ 
 
69,353 (30%) 
48,353 (21%) 
25,388 (11%) 
24,705 (11%) 
 
433,799 (31%) 
268,236 (19%) 
128,113 (9%) 
94,550 (7%) 
 
226,113 (32%) 
128,799 (18%) 
58,072 (8%) 
43,377 (6%) 
 
92,807 (31%) 
60,330 (20%) 
29,930 (10%) 
23,321 (8%) 
 
122,583 (31%) 
87,023 (22%) 
70,774 (18%) 
25,410 (7%) 
 
944,655 (32%) 
592,741 (20%) 
312,277 (10%) 
211,363 (7%) 

Page 12 of 20 (V.10/27/2015)  
intensive behavioral therapy for obesity) and 3 years after the last policy change (Pennsylvania Medicaid expansion) 
under study.  
 
 
 
Data Transfer. The PaTH network has established an operational data infrastructure with the necessary technical 
safeguards as agreed upon in the PaTH Data Use Agreement (DUA) for sharing and analyzing data while addressing data 
confidentiality and security concerns. PaTH has deployed two mechanisms for storing, protecting, and sharing data (as 
described previously under PaTH Governance and Regulatory Issues): (1) data with PHI are stored and protected behind 
each institution’s firewall in the distributed data network and (2) de-identified data are sent to the PaTH data center at 

Page 13 of 20 (V.10/27/2015)  
the University of Pittsburgh (CERC-DC). Once data integration is accomplished at each site, sites will remove all PHI and 
send the de-identified version of the integrated data to the CERC-DC through PaTH’s virtual private network/secure file 
transfer protocol (VPN) which has been operational for data transmission since November 2014. Data analysis can then 
be performed via secure remote computing using standard statistical software packages (e.g., SAS). This model ensures 
that the highest level of privacy and confidentiality for the data that are maintained at each site, the CERC-DC, and at 
Penn State.  
 
 
8.0 
Statistical Plan 
Descriptive statistics will be generated to describe the characteristics of different cohorts of interest. The diabetes 
related outcomes will be summarized at the individual level on a yearly basis. There will be binary outcomes (Yes/No) 
such as controlled diabetes, controlled blood pressures, receiving annual eye exam, receiving obesity and/or nutritional 
counseling, and count outcomes such as numbers of clinic visits, emergency department visits and hospitalization, and 
continuous outcomes such as cost of healthcare utilizations. The distributions of outcome measures will be examined by 
using minima, maxima, ranges, medians, quartiles, means, and standard deviations for continuous variables, and 
frequency and contingency tables for categorical variables.  
 
To evaluate the impact of policy changes on these outcomes, we will examine how these outcomes change over time, in 
response to the policy changes. As descriptive analyses, we will plot the mean trajectory of each yearly outcome at clinic 
level, health system level and state level. The statistical modeling of patterns of changes in individual level outcomes will 
be carried out through multi-level mixed-effects models.12-15 The mixed-effects model is a common and popular 
modeling technique for longitudinal data. A mixed-effects model can accommodate within- and between-subjects 
variability, as well as serial correlations. In addition, it has the flexibility to incorporate time-dependent covariates, 
incomplete data, and heteroscedasticity of the variances and correlations. The mixed-effects model will be specified in a 
multi-level fashion so that different levels of variability (e.g., individual characteristics, social environment, built 
environment) can be taken into account. The pattern of changes in the outcome will be assessed for pre- and post- 
periods, respectively, based on the piecewise/segmented regression models. The slope of each segment indicates the 
trend of change in diabetes outcome in that period. Therefore, the change in the trend/slope post-policy 
implementation may reveal the actual impact of the new policy controlling for baseline level and trend. Such modeling 
strategies share the same spirit of interrupted time series analysis. While the classical interrupted time series design 
often generates a single long series of data, we have a large number of short series from each individual subject, namely, 
longitudinal data. Depending on the types of the outcomes, we will specify mixed-effects models based on logistic 

Page 14 of 20 (V.10/27/2015)  
regression for a binary outcome, Poisson regression for a count outcome and linear regression for a continuous outcome 
as detailed below.  
 
In the multi-level modeling, the first-level unit is the yearly measurement for the individual subject (pre- and post-
policy), the second–level unit is the individual subject, the third-level unit is the health system or clinic within the health 
system (cluster), and the fourth-level unit is the state. We set year 2014 as time origin for Utah when considering the 
policy impact of the ACA. Let Yijkt denote the binary response of having controlled diabetes at year t since policy changes 
for the kth subject within the jth cluster of the ith state, t=-5, -4, …, 0,  1, …, 5, i = 1, 2, 3; j = 1, 2, …, ci, and k = 1, 2, …, nij, 
where ci is the number of clusters within the ith state and nij is the sample size within the jth cluster of the ith state. An 
individual subject may not be in the system for all 11 timepoints of measurement, so t will have a smaller range of values 
for that individual subject. Also due to the different timeline of policy changes, the number of years before and after 
policy changes may vary. The probability of having controlled diabetes, μijkt = E(Yijkt)= Pr[Yijk = 1], can be described by the 
following segmented logistic regression model  
 
          logit μijkt = log {Pr[Yijk = 1]/ Pr[Yijk = 0]} 
= 0ijk + (t +5) 1ijk   
 
                 if -5 t  0, pre-period before policy changes 
= 0ijk + 5 1ijk  + t 2ijk  
 
        
        if  0 t  5, post-period after policy changes 
 
where mijk (m= 0, 1, 2) are subject-specific regression parameters, with 0ijk being the log odds of having controlled 
diabetes at t = -5, and 1ijk and 2ijk being the slopes (annual change in log odds) for the pre- and post-periods, 
respectively. In the framework of a mixed-effects model, each mijk is modeled by 
 
mijk = xmijkTm + zijT 1ij + 2mijk 
 
where xmijk is the vector of regressors for the fixed effects, m is the corresponding vector of fixed-effects parameter 
coefficients, zij is a vector of cluster-level regressors for the random effects for the jth cluster of the ith state, 1ij is the 
cluster-level random-effect coefficients and is common to all m, and 2mijk is the subject-level random-effect coefficients 
associated with the parameter m for the kth subject within the jth cluster. The random effects are assumed to follow a 
multivariate normal distribution with mean zero. Because the vector xmijk may include subject-level, cluster-level and 
state-level exposure variables, the fixed effects parameter vectors 1 and 2 represent the effects of different exposure 
variables on the annual changes in the pre- and post-periods. Thus, we may perform statistical tests to examine whether 
there are differences in trends between the pre- and post-periods overall and for each state, and whether the patterns 
of changes differ between the states. 
Similarly for a count outcome Yijkt (e.g., number of clinical visits), we define the expected value μijkt = E(Yijkt) and apply a 
Poisson regression model based on the natural log link function. The log expected number of clinical visits, log(μijkt), can 
be modeled with the aforementioned segmented mixed-effects model. The use of an offset term in the models yields 
the estimates of the rate of clinical visits rather than the mean number of visits. The fixed-effect parameters, 1 and 2, 
represent the effects of exposure variables on the annual changes in log of incidence rates for the pre- and post-periods, 
respectively. For a continuous outcome Yijkt, we will model the mean, μijkt = E(Yijkt), with the mixed-effects model and the 
parameters 1 and 2 indicate the effects of exposure variables on the annual changes in the outcomes for the pre- and 
post-periods, respectively. All final statistical models will be assessed with respect to the goodness-of-fit and the 
appropriateness of model assumptions. Statistical software SAS 9.4 and R environment will be used to implement the 
proposed analyses. 
 
Furthermore, we will use the similar models as described above to evaluate post-policy impact of obesity screening and 
counseling (number of visits) on the outcomes such as weight loss, diabetes incidence, and diabetes outcomes (including 
hemoglobin A1c, controlled blood pressure, use of a statin medication). The outcome data collected after the universal 
coverage change (i.e., t=0, 1, ... 6) will be used to estimate the trend of change. The number of screening and counseling 
will be used as a predictor in the mixed effects models. The effects of patient and practice characteristics will also be 
examined in the models. Statistical software SAS 9.4 and R environment will be used to implement the proposed 
analyses. 
 

Page 15 of 20 (V.10/27/2015)  
 
 
 
Subgroup analyses 
 
Due to the heterogeneity of the population and a dynamic cohort in our study, we will investigate subgroup analyses to 
assess how the policy impact varies across different subgroups. Following the general modeling approaches as described 
above, we will examine the benefits of policy changes for different subgroups including (1) patients with insurance 
throughout the study period, (2) patients who obtained insurance after the policy changes, (3) patients without 
insurance throughout the study period, (4) patients newly enrolled in the system after policy changes, and (5) other 
subgroups of interest according to gender, age (e.g., age 65 and older), race-ethnicity. Meta-analysis has been a 
powerful approach to combining the effects of interest across different studies, different populations, and different 
subgroups.16,17 We will adopt this method to evaluate the average impact of policy changes across subgroups. A forest 
plot will be generated to reveal how the addition of a subgroup to the meta-analysis may affect the average policy 
impact.  
 
Propensity scores matching  
 
In the proposed modeling framework above, we adjust for the subject-level and cluster-level differences by including the 
exposure variables at different levels as covariates in the models. We also will consider a secondary analysis with a 
propensity scores matching approach to adjusting for these differences.18,19 A wide array of patient measures in the EHR, 
including demographics, insurance coverage, medical co-morbidities, health behaviors, and information on use of health 
care services, will be used to calculate the propensity scores. Propensity score-based stratification analysis will be 
performed to evaluate the overall impact of health policy using the modeling framework similar to that described above. 
 
Analyses of diabetes outcomes at population level 
 
The primary analysis of our study focuses on the individual-level outcomes. The statistical models yield the estimate of 
average change at individual level post policy implementation. Given the information in the EHR data, we can also 
aggregate the diabetes outcomes at community level, clinic level, etc. For example, the proportion of patients with 
controlled diabetes can be obtained for each clinic and used as the outcome variable in the statistical modeling. The 
proposed mixed-effects modeling framework are still applicable in this case. The statistical analyses can be performed in 
a similar fashion to that for the individual-level outcomes. 
 
For example, our proposed statistical analysis plan can be easily modified to compare the differences in weight loss and 
diabetes outcomes (including diabetes control, controlled blood pressure, use of a statin medication, receipt of an 
annual eye exam and annual urinary microalbumin test, and lower extremity amputations) between rural and urban 
areas. As stated in the proposal, although our main analysis is on individual-level outcomes, aggregated outcomes at the 
community- or county-level can also be extracted from the electronic health record (EHR) data. For example, the 
proportions of patients with controlled diabetes in each county at each year can be obtained and used as the outcome 
variable after arcsine-square root transformation in the statistical modeling. We can evaluate the rural/urban effects on 
the pattern of changes in diabetes outcomes over years by including county-level characteristics such as rural vs. urban 
in the mixed-effects models as fixed effects. The time origin in the analysis will be the beginning of the study period, 
rather than the time when insurance policy changes occurred. The counties sharing similar characteristics (e.g., access to 
the same health system) will be considered as a cluster and the clustering effect will be accounted for in the mixed-
effects model analysis by including cluster-level random effects. Instead of using segmented regression models to 
evaluate the trend in diabetes outcomes before and after the policy change, we will consider linear or non-linear trends 
in the diabetes outcomes and allow rural and urban counties to have different patterns of changes in the models. 
 
Statistical Power. Given the very large sample sizes that are anticipated for the research studies (more than 320,000 
patients with diabetes and 2,000,000 patients at-risk for diabetes), there is tremendous statistical power to detect very 
small effect sizes for individual-level exposure variables. Therefore, the clinical investigators on this project will need to 

Page 16 of 20 (V.10/27/2015)  
examine each statistically significant result and determine whether it is clinically significant. Furthermore, such large 
sample sizes ensure the robust estimation results from the proposed multi-level statistical modeling which involves large 
number of regression coefficient and covariance parameters. The major benefit of the large sample size for each 
research study is that it provides sufficient statistical power for investigating effects of interest within subgroups that 
might be constructed according to age, race-ethnicity, cohort decompositions, etc. 
 
9.0 
Confidentiality, Privacy and Data Management  
 
See Data Plan Review Form HRP-598 
 
9.1 
Confidentiality 
 
9.1.1 
Identifiers associated with data and/or specimens 
 
 
Names; 
 
All geographical subdivisions smaller than a State, including street address, city, 
 county, precinct, zip code, and their equivalent geocodes; 
 
Birth date, admission date, discharge date, date of death;  
 
Phone numbers; 
 
Fax numbers; 
 
Electronic mail addresses; 
 
Social Security numbers; 
 
Medical record numbers; 
 
Health plan beneficiary numbers;  
 
Account numbers; 
 
Certificate/license numbers; 
 
Vehicle identifiers and serial numbers, including license plate numbers; 
 
Device identifiers and serial numbers; 
 
Web Universal Resource Locators (URLs); 
 
Internet Protocol (IP) address numbers; 
 
Biometric identifiers, including finger and voice prints; 
 
Full face photographic images and any comparable images (including videos);  
 
Any other unique identifying number, characteristic, or code (note this does not  
mean the unique code assigned by the investigator to code the data); 
 
Other (please specify): 
 
9.1.1.1 Use of Codes, Master List 
   
 
 
 
 
N/A 
 
9.1.2 
Storage of Data for This Study 
N/A 

Page 17 of 20 (V.10/27/2015)  
9.1.3 
Access to Data for This Study 
N/A 
9.1.4 
Transferring Data for This Study 
N/A 
9.2 
Subject Privacy 
N/A 
10.0 Risks 
Only de-identified patient level data will be collected, so while loss of confidentiality is a risk, it’s 
unlikely. To minimize the likelihood of a breach in confidentiality, we will follow standard PaTH 
procedures with regards to data management. Individual health data will be de-identified at each 
institution and loaded into the PaTH i2b2 instance, transferred, and stored at the University of 
Pittsburgh CERC-DC. 
 
11.0 Potential Benefits to Subjects and Others 
 
11.1 
Potential Benefits to Subjects 
None 
 
11.2 
Potential Benefits to Others 
Potential benefits to others include understanding the effectiveness of obesity counseling benefits for 
patients with type 2 diabetes an at-risk for diabetes.  
 
12.0 Other Approvals 
 
12.1 Other Approvals from External Entities 
 
N/A 
 
12.2 Internal PSU Committee Approvals 
 
Check all that apply: 
  Anatomic Pathology – Hershey only – Research involves the collection of tissues or use of pathologic 
specimens. Upload a copy of the Use of Human Tissue For Research Form on the Supporting Documents 
page in CATS IRB. This form is available on the IRB website at: 
http://www.pennstatehershey.org/web/irb/home/resources/forms  
  Animal Care and Use – All campuses – Human research involves animals and humans or the use of human 
tissues in animals 
  Biosafety – All campuses – Research involves bio-hazardous materials (human biological specimens in a PSU 
research lab, biological toxins, carcinogens, infectious agents, recombinant viruses or DNA or gene therapy.  
  Conflict of Interest Review – All campuses – Research has one or more of study team members indicated as 
having a financial interest. 
  Radiation Safety – Hershey only – Research involves research-related radiation procedures. All research 
involving radiation procedures (standard of care and/or research-related) must upload the Radiation Review 
Form on the Supporting Documents page in CATS IRB. This form is available on the IRB website at: 
http://www.pennstatehershey.org/web/irb/home/resources/forms  
  IND/IDE Audit – All campuses – Research in which the PSU researcher holds the IND or IDE or intends to hold 
the IND or IDE. 
  Scientific Review – Hershey only – All investigator-written research studies requiring review by the convened 
IRB must provide documentation of scientific review with the IRB submission. The scientific review 

Page 18 of 20 (V.10/27/2015)  
requirement may be fulfilled by one of the following: (1) external peer-review process; (2) 
department/institute scientific review committee; or (3) scientific review by the Clinical Research Center 
Advisory committee.  Note: Review by the Penn State Hershey Cancer Institute Scientific Review Committee 
is required if the study involves cancer prevention studies or cancer patients, records and/or tissues. For 
more information about this requirement see the IRB website at: 
http://www.pennstatehershey.org/web/irb/home/resources/investigator  
 
13.0 Multi-Site Research 
 
13.1 
Communication Plans 
The lead investigative team (Core PI, co-PIs, core project manager) meet on a bi-weekly basis to create 
and modify the protocol and well as address any issues that arise from any of the sites. The core project 
manager and core Principal Investigator will work together to create a standard protocol and 
disseminate it among the sites to submit to each institution’s IRB. The core project manager will also 
coordinate and review any modifications to the protocol with both the lead team and with all the site 
project managers (there is a project manager for each site) to submit the appropriate changes to their 
IRB. 
 
13.2 
Data Submission and Security Plan 
To minimize the likelihood of a breach in confidentiality, we will follow standard PaTH procedures with 
regards to data management.  Individual health data will be de-identified and loaded into the PaTH i2b2 
instance, transferred, and stored at the University of Pittsburgh CERC-DC. No identifiable patient 
information will leave institutional firewalls. Data analysis by Penn State investigators will occur through 
secure remote computing. No patients will be contacted for this study. 
 
13.3 
Subject Enrollment 
The lead investigative team will meet regularly and communicate with the PaTH project manager to 
coordinator data extraction. 
 
13.4 
Reporting of Adverse Events and New Information 
N/A 
 
13.5 
Audit and Monitoring Plans 
 
There are several processes in place to ensure all site investigators conduct the study appropriately.  
First, PaTH has multiple internal procedures to ensure data is accurate. Given the study is reliant on the 
PaTH Network, a regular meeting schedule will be determined to ensure the data is being extracted and 
reported to meet study requirements in accordance with protocol. Local procedure will include the Penn 
State study team (PI, core project manager) jointly overseeing the auditing and monitoring plans. 
Specifically, the PCORI milestones have been back-dated to allow one month for review by the PI and 
core project manager. Data will be checked for completeness and accuracy, based on expected values 
and ranges. Biweekly meetings between the PI and core project manager will be used to review protocol 
and site progress and ensure proper conduction of the study. 
 
14.0 Adverse Event Reporting 
 
14.1 
Reporting Adverse Reactions and Unanticipated Problems to the Responsible IRB 
 
In accordance with applicable policies of The Pennsylvania State University Institutional Review Board 
(IRB), the investigator will report, to the IRB, any observed or reported harm (adverse event) 
experienced by a subject or other individual, which in the opinion of the investigator is determined to be 

Page 19 of 20 (V.10/27/2015)  
(1) unexpected; and (2) probably related to the research procedures. Harms (adverse events) will be 
submitted to the IRB in accordance with the IRB policies and procedures. 
 
15.0 Study Monitoring, Auditing and Inspecting 
 
15.1 
Auditing and Inspecting 
 
The investigator will permit study-related monitoring, audits, and inspections by the Penn State quality 
assurance program office(s), IRB, the sponsor, and government regulatory bodies, of all study related 
documents (e.g., source documents, regulatory documents, data collection instruments, study data 
etc.).  The investigator will ensure the capability for inspections of applicable study-related facilities 
(e.g., pharmacy, diagnostic laboratory, etc.) 
 
16.0 Future Undetermined Research: Data Banking 
 
N/A.  This study is not collecting or retaining identifiable data.   
 
16.1 
Data being stored 
N/A 
16.2 
Location of storage 
N/A 
16.3 
Duration of storage 
N/A 
16.4 
Access to data  
N/A 
16.5 
Procedures to release data  
N/A 
16.6 
Process for returning results 
N/A 
 
17.0 References 
 
1. 
Ogden CL, Carroll MD, Fryar CD, Flegal KM. Prevalence of Obesity Among Adults and Youth:  United 
States, 2011-2014, 2015. 
2. 
U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and 
Prevention, Statistics NCfH. Healthy People 2010. Washington, D.C., 2000. 
3. 
Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 
1999-2008. Jama 2010; 303(3): 235-41. 
4. 
Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health 
risk factors. JAMA 2001; 289(1): 76-9. 
5. 
Thompson D, Edelsberg J, Colditz GA, Bird AP, Oster G. Lifetime health and economic consequences 
of obesity. Archives of internal medicine 1999; 159(18): 2177-83. 
6. 
Centers for Disease Control and Prevention. National Diabetes Statistics Report:  Estimates of Diabetes 
and Its Burden in the United States, 2014. Atlanta, GA: U.S. Department of Health and Human Services, 2014. 
7. 
Healthy People.gov. HealthyPeople 2020 Diabetes. 2015. http://www.healthypeople.gov/2020/topics-
objectives/topic/diabetes (accessed February 1 2015). 
8. 
Patient Protection and Affordable Care Act (‘‘PPACA’’). 2010. 
9. 
Schoen C, Osborn R, Huynh PT, et al. Primary care and health system performance: adults' experiences 
in five countries. Health Aff (Millwood) 2004; Suppl Web Exclusives: W4-487-503. 
10. 
Services CfMM. Decision Memo for Intensive Behavioral Therapy for Obesity. 2015. 
http://www.cms.gov/medicare-coverage-database/details/nca-decision-memo (accessed February 14 2015). 

Page 20 of 20 (V.10/27/2015)  
11. 
Krishna S, Gillespie KN, McBride TM. Diabetes burden and access to preventive care in the rural 
United States. J Rural Health 2010; 26(1): 3-11. 
12. 
Keppel KG, Pearcy JN, Klein RJ. Measuring progress in Healthy People 2010. Healthy People 2010 
Stat Notes 2004; (25): 1-16. 
13. 
Rural Healthy People 2020 Texas A&M Health Science Center School of Public Health, Southwest 
Rural Health Research Center; 2015. 
14. 
Massey CN, Appel SJ, Buchanan KL, Cherrington A. Improving diabetes care in rural communities:  An 
overview of current initiatives and a call for renewed efforts. Clinical Diabetes 2010; 28(1): 20-7. 
15. 
Nawal Lutfiyya M, Patel YR, Steele JB, et al. Are there disparities in diabetes care? A comparison of 
care received by US rural and non-rural adults with diabetes. Primary Health Care Research & Development 
2009; 10: 320-31.