Impact of Medicaid Health Home on Patients With Diabetes in New York City
Conditions: Type 2 Diabetes Mellitus
Sponsor: Icahn School of Medicine at Mount Sinai

Impact of Medicaid Health Home on Patients With Diabetes in New York City 
NCT02713321
Victoria Mayer, MD
Document Date: 8/17/2020

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PROTOCOL 
ISMMS GCO 15-1545 
The Impact of Medicaid Health Homes on patients with diabetes 
8/17/2020 
1) Objectives
We plan to examine the effects of New York State’s (NYS) Medicaid Health Home (HHs) program (a
population-targeted intervention at the state level) on the process of care, outcomes, and racial/ethnic
disparities among low-income residents of New York City (NYC) with Type II diabetes mellitus.  In 2012,
NYS Medicaid initiated HHs with the “triple aim” of 1) improving quality, 2) improving outcomes, and 3)
reducing costs of care for high-need patients with chronic diseases. The program was designed to
enhance care and outcomes for patients with two or more chronic conditions, such as diabetes and
coronary disease, HIV, and/or a serious mental health condition.  HHs provide these patients with
coordination of care, by promoting collaboration among health care providers, social service agencies,
community-based organizations, and health insurance plans. Taken together, the roll-out, timing,
scope of the program, which currently serves over 60,000 patients in NYC, and the availability of both
Medicaid data and unique clinical data in NYC through the NYC-Clinical Data Research Network and the
Health Evaluation and Analytics Lab provides an opportunity to evaluate the impact of HHs on diabetes
management, complications, and health disparities, using quasi-experimental methods.
Similar care management programs have been shown, in studies of limited scope, to improve 
intermediate outcomes for patients with diabetes. However, the impact of HHs on processes of care 
and outcomes among low-income patients with diabetes mellitus in large, diverse, and complex urban 
settings remains largely unexplored.  Additionally, current research on the impact of HHs, both in NYS 
and elsewhere, does not incorporate individual-level clinical data on chronic disease management. As 
a result, whether the Medicaid HHs program has impacted the health of patients with diabetes has 
not been examined. It is also not known how interventions of this scope will affect long-standing racial 
and ethnic disparities in the quality of care and outcomes of individuals with diabetes. Since similar 
programs are being implemented nationally, it now is critically important to evaluate the effects of 
HHs on the process and outcomes among patients with diabetes, a condition with multiple 
complications, which are disproportionately prevalent among racial and ethnic minorities and low-
income populations in the United States. 
We plan to use two complementary sources of longitudinal individual-level data: 1) NYS Medicaid 
administrative and claims data and 2) the New York City-Clinical Data Research Network (NYC-CDRN), 
funded by the Patient-Centered Outcomes Research Institute (PCORI). The NYC-CDRN has identified a 
cohort of patients with diabetes (approximately 90,700 Medicaid enrollees with diabetes) from 7 large 
health systems based on electronic health record data; and it has developed a common data model that 
includes clinical variables related to the process and outcomes of care of patients with diabetes.  Using 
information available from the two complementary data sources and quasi-experimental methods, we 
will assess the impact of Medicaid HHs in NYC.  
 Among adult Medicaid patients with diabetes in NYC, we propose: 
Aim 1: To assess the impact of HHs on the process of care for patients with diabetes.  
Aim 2: To assess the impact of HHs on the health outcomes of patients with diabetes. 

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Aim 3:  To assess the impact of HHs on racial and ethnic disparities in the process of care and outcomes 
among Medicaid patients with diabetes. 
Our sample will include Medicaid enrollees with diabetes who are seen at NYC-CDRN sites and in HHs 
(estimated 6,500-20,000) and a matched comparison group of Medicaid enrollees with diabetes who are 
part of the NYC-CDRN, but not HHs. We plan to develop a set of propensity score matched controls, 
based on a set of patient, practice, and market-level (zip-code) characteristics. We will use quasi-
experimental methods to investigate the magnitude of the effects of the HHs program between 2010 
and 2017. This secondary data analysis is referred to as Activity 1 in this protocol. In addition, we will 
gather information regarding the patient experience of HHs through interviews (Activity 2). We will also 
utilize interviews with HH leaders and care managers from NYC HHs to further characterize these entities 
(Activity 3). Columbia University will participate in only Activity 1.   
8/20/20 update: Apart from evaluating the impact of the NYS Medicaid HH program on the process and 
outcomes of care for low-income NYC residents with diabetes, compared to non-enrolled residents with 
similar conditions and utilization histories, we have received funding to extend this work to examine the 
impacts of the COVID-19 pandemic on this vulnerable population, a majority of which is Black or Latinx. 
We hypothesize that HH participation will reduce pandemic-related disruptions in access to health care 
and social services and improve health outcomes among patients with diabetes in NYC. Using a quasi-
experimental difference-in-differences design, we will contrast access and utilization of health care and 
social services and health outcomes among HH enrollees and non-enrollees during a baseline period 
prior to the pandemic (CY2019) with each month of the pandemic time period (2020).  
Aim 4: Using Medicaid claims data, we will estimate the impact of HH enrollment compared to usual 
care among Medicaid-insured patients with diabetes and other chronic conditions on access to health 
care and social services and COVID-19 and non-COVID-19 clinical outcomes, as well as racial/ethnic 
disparities in these outcomes, before and during the pandemic in NYC. 
Using Medicaid claims only, we will identify HH participants with diabetes living in NYC in 2019, and use 
matching and weighting methods to develop a non-HH comparison group who were similar to HH 
enrollees in their demographics, comorbidities and utilization histories. We will then conduct difference-
in-differences analyses using an “event study” framework to compare the pre-COVID time period 
(CY2019) to each month of the post-COVID period (2020) in terms of access, utilization, and clinical 
outcomes between the HH and matched non-HH group, overall and stratified by race/ethnicity (Activity 
4).  
2) Background
Diabetes Prevalence 
Type II diabetes mellitus is a common chronic disease, with an overall prevalence of 9.3% in the US (1). 
In New York City (NYC), the overall prevalence of diabetes is somewhat higher, 10.8%; and, the 
prevalence of diabetes are higher among racial and ethnic minorities, compared to Whites (2):  among 
Hispanics, the prevalence is 15.2%; among non-Hispanic Blacks 12.8%; and, among non-Hispanic Whites 
7.1%.(3)  The prevalence of diabetes also is higher among those of lower socioeconomic status (SES), 

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compared to those of higher SES: among those living at <100% of the Federal Poverty Level (FPL) the 
prevalence is 14.8%; and, among the highest income group (>/=600% FPL) 4.7%.(3) 
Diabetes Complications 
The complications of diabetes impact health and well-being.  In addition to the direct metabolic 
consequences, diabetes increases the long-term risk of cardio-renal diseases, including coronary heart 
disease, stroke, heart failure, renal disease, retinopathy and blindness; and these complications 
contribute to health disparities.  In NYC, mortality related to diabetes is significantly higher among non-
Hispanic Blacks and Hispanics and among individuals living in high-poverty neighborhoods, compared to 
non-Hispanic Whites and those living in low-poverty neighborhoods respectively.(4) Individuals living in 
high-poverty neighborhoods also have higher rates of hospitalizations for diabetes-related 
complications.(5)  
Diabetes care 
In order to improve health care and outcomes, reduce costs, and address health disparities related to 
diabetes, a number of health care interventions have been implemented by health systems and 
insurance providers, many focused on care management and care coordination. Increasingly, as health 
systems are incentivized to address health on a population level, interventions are also designed to 
address health determinants outside of the health care system, such as social and environmental factors 
and health behavior, through collaboration with community-based organizations (CBOs).  Prior research 
among racially and ethnically diverse populations has shown that care management interventions 
delivered by primary care providers, registered nurses, community health workers, and endocrinologists 
can improve intermediate outcomes, such as hemoglobin A1c (HbA1c).(6-9) However, evidence does not 
presently exist on the long-term outcomes of care management and care coordination for patients with 
diabetes. Peek et al. describe many interventions that involve community-health system partnerships. 
Examples include clinics working with community-health workers, physician-referrals to community-
based education, food “prescription” programs, and the integration of community-based resources into 
broad quality improvement initiatives.(10) While evaluations of some of these have shown promising 
effects on intermediate outcomes, there is little data about the overall effects of such interventions to 
date, especially in large, diverse, urban settings.  
Medical homes 
Care management and community-health system partnerships commonly exist within the patient-
centered medical home (PCMH) model. The Agency for Healthcare Research and Quality (AHRQ) defines 
the PCMH as a model of care that offers comprehensive care of each patient’s physical and mental 
health through a team of providers, is patient-centered, coordinates care across the health system, 
provides accessible services, and engages in continual quality and safety improvement evaluation and 
activities.(11) Consensus principles of the PCMH, building on the backdrop of 40 years of primary-care 
practice redesign, were established in 2007 by the American College of Physicians, the American 
Academy of Family Physicians, the American Academy of Pediatrics, and the American Osteopathic 
Association.(12) A growing number of providers are part of PCMHs, serving several million patients.(13, 
14) However, there are large variations in the definition, and implementation of the PCMH.(15) This
variation has made it difficult to assess the impact of the PCMH on quality of care and clinical outcomes.
Affordable Care Act and Medicaid Health Homes 

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As part of the Patient Protection and Affordable Care Act (ACA) of 2010, “health homes” were 
authorized for care management and coordination for individuals enrolled in Medicaid. Under the ACA, 
six health home services are eligible for reimbursement: comprehensive care management, care 
coordination and health promotion, comprehensive transitional care, patient and family support, 
referral to community and social support services, and use of health information technology to link 
services.(16) As such, the Medicaid Health Home (HH) includes interventions previously tested among 
individuals with diabetes. However, the payment reforms associated with these new care delivery 
mechanisms add another aspect to the intervention. In addition, these types of interventions are now 
being expanded to include a larger patient population- those eligible for HHs. In order to be included in a 
HH, patients need to have chronic conditions, including serious mental illness, substance use disorders, 
or multiple chronic diseases, including diabetes. As of November 2014, 20 states had approved plans for 
HHs.(17)  
New York State (NYS) established a Medicaid Health Home program in 2012, with the New York State 
Health Home State Plan Amendment (SPA). In New York State at the end of 2014, over 5 million low-
income individuals were enrolled in Medicaid, including over 3 million of NYC’s estimated 8.4 million 
residents.(18, 19) The State estimated in 2012 that approximately 1 million individuals would be eligible 
for the HH program, including 418,677 Medicaid recipients with mental health or substance use 
disorders, and 386,399 participants with other chronic conditions. HHs were rolled out in three phases 
during 2012, with Phase I in January (which included the Bronx and Kings (Brooklyn) Counties), Phase II 
in April (which included New York (Manhattan), Queens and Richmond (Staten Island), the remaining 
counties in NYC), and Phase III in July.(20) As of August 2014, 40,776 individuals were enrolled in 
NYC.(21, 22) Around 35,000 Medicaid enrollees across NYS were part of precursor care management 
programs (including the Office of Mental Health’s Targeted Case Management Program (for those with 
severe mental illness), Managed Addiction Treatment Services Program, and the Comprehensive 
Medicaid Case Management Program (for people living with HIV/AIDS), which are all in the process of 
converting to HHs.(16)   
The identification of individuals eligible for HHs is initiated by one of two processes: a NYS Department 
of Health (DOH) predictive algorithm utilizing Medicaid services data, which prioritizes individuals based 
on risk of future hospitalization; and community or “bottom up” referrals from healthcare providers, 
community-based care management providers, including harm reduction providers, and other agencies 
such as those who serve individuals re-entering the community from jail or prison. Enrollment in HHs 
has been slower than expected, and several challenges have been identified and are being addressed by 
the NYS DOH. These include inaccuracies in the lists of targeted individuals, challenges in the 
recruitment process, and individuals electing not to participate in the program.(16) The new program 
may put stress on already under-resourced facilities, by increasing the complexity of billing, the 
requirements around health information technology (HIT), the caseloads of facilities and case managers, 
and, in some cases, lowering reimbursement for care management services. Another significant change 
brought about by HHs is the expanded scope of health conditions of clients served by care management 
agencies that formerly provided case management for people living with HIV/AIDS, substance use 
disorders, or serious mental illness (P. Schafer, personal communication, March 9, 2015).   
Medicaid Health Homes and Diabetes Management 
HH-eligible patients with diabetes are likely to be an especially vulnerable population, and one where 
disparities in access and quality are starker. The prevalence of diabetes among patients with serious 

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mental illness (SMI) is higher than in the general population. Increased mortality among those with SMI 
is partially attributable to higher rates of cardio-metabolic disease.(23, 24) Patients with SMI and 
diabetes have been found to be less likely to receive indicated services and care, and more likely to be 
admitted to the hospital with ambulatory-care sensitive conditions when compared to those without 
SMI.(23, 25) Over half of patients with diabetes have at least one other chronic condition, and a large 
proportion of these have two or more.(26, 27) Suffering from multiple chronic diseases puts individuals 
at greater risk of preventable hospitalizations, poor functional status, higher cost care, and increased 
mortality.(26, 28) 
 
HH enrollment would ideally improve the processes and the outcomes of care; and it would reduce 
health disparities (related to the process and outcomes of care) for individuals with diabetes. Through 
coordination of care, intensive case management, and attention to social needs, individuals with 
diabetes could develop better disease self-management, receive proper specialist evaluation and 
evidence-based regimens, and work with a team that identifies opportunities to bolster social support.   
As the HH intervention is applied differently at different facilities, these effects may vary across 
institutions. In NYS, evaluations are currently being conducted of the overall HHs program and of those 
participants with substance use disorders and those with HIV; however, by focusing on individuals with 
diabetes, the proposed research would add substantially to knowledge about the effect of HHs on 
patients on an important common chronic condition, diabetes. 
 
Impact of COVID-19 on Medicaid Health Homes 
 
In spring 2020, New York City (NYC) was the American epicenter of the COVID-19 pandemic. In addition 
to direct health effects, the pandemic also had major impacts on the delivery of health care and social 
services. Low-income patients with diabetes, especially among racial/ethnic minorities, are more 
vulnerable to the serious morbidity and mortality associated with COVID-19; and, they are more likely to 
experience a lack of access to health care and social services. Outpatient medical care essentially shut 
down for in-person visits during the pandemic, and access to primary care, disease management, and 
medications continue to be disrupted. Limited mobility and social determinants, such as access to 
transportation, employment, internet connectivity, and competing needs, also likely impacted access to 
health care and social services and clinical outcomes during the pandemic. In response, New York State 
(NYS) Medicaid adapted regulations to allow Health Homes (HHs) to bill for remote delivery of care 
management services. As a result, HH participants are likely to have higher rates of health care and 
social service utilization and better health outcomes, compared to similar HH non-participants. 
 
The proposed study will be among the first to utilize the New York City Clinical Data Research Network 
(NYC-CDRN) data and infrastructure to examine the effects of a population-level health policy, the 
implementation of the Medicaid HH program, on the process, outcomes, and disparities of care for a 
common chronic condition, diabetes.   
 
3) Setting of the Human Research 
 
The primary research team is located at the Icahn School of Medicine at Mount Sinai. Other members of 
the team are located at Weill Cornell Medicine and the New York Academy of Medicine (NYAM) (Co-PI 
David Siscovick). NYAM is a non-profit center of interdisciplinary research, evaluation, policy, and 

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program initiatives focused on urban health.  The study is a collaboration with the New York City Clinical 
Data Research Network and the HEAL at NYU School of Medicine.  
 
The New York City Clinical Data Research Network (NYC-CDRN) was founded with support from the 
Patient-Centered Outcomes Research Institute (PCORI) to join its PCORnet program. The mission of the 
NYC-CDRN is to create an accessible, sustainable, scalable clinical data network to facilitate patient-
centered research, learning healthcare systems and a national research network (PCORnet). Efforts are 
focused on five goals: 
1. Creating thoughtful privacy policies that protect the identities of individual patients, 
clinicians, and health systems; 
2. Establishing a de-identified database with clinical data on at least one million New 
Yorkers; 
3. Engaging patients, caregivers, and clinicians in network governance and decision-making; 
4. Embedding research activities in the delivery of healthcare to minimize the burden on 
patients, clinicians, and health systems; and 
5. Developing a high-functioning, sustainable governance structure. 
 
NYC-CDRN’s program builds on the joint efforts of 22 organizations – including academic medical 
centers, associated Clinical and Translational Science Centers, federally qualified health centers, regional 
health information organizations (RHIOs), research institutions, consumer advocacy groups and 
government agencies - to develop a patient-centered, sustainable network for conducting research 
across New York City and the nation and facilitating a learning healthcare system. 
 
The Health Evaluation and Analytics Lab (HEAL) promotes applied research to help health sector 
organizations evaluate initiatives that seek to improve health outcomes among populations and to 
improve health care delivery practices.  HEAL has built a unique New York State Medicaid claims file. 
This large data system allows for a wide range of evaluative work focused on improving care and 
outcomes for the most vulnerable New Yorkers, who rely on Medicaid to pay for their medical care 
needs. The data set also allows providers to better track the services received by their patients. The 
effort to create the claims file analytical system was financially supported by the New York State Health 
Foundation and was done in close collaboration with the New York State Department of Health. 
 
 
Participating organizations include:* 
Health Systems/Facilities: 
 Clinical Directors Network 
 Columbia University 
 New York-Presbyterian Hospital 
 NYU Langone Medical Center 
 Montefiore Medical Center 
 Mount Sinai Health System 
 Weill Cornell Medical College 
 Charles B. Wang Community 
Health Center 
 Community Healthcare Network 
Infrastructure: 
 Biomedical 
Research Alliance 
of NY (BRANY) 
 Bronx RHIO (BRIC) 
 Healthix RHIO 
 A Weill Cornell 
approved vendor 
(NYC-CDRN 
Central 
Repository) 
 Rockefeller 
Other Partners: 
 Center for Medical 
Consumers 
 Cornell NYC Tech 
Campus 
 NY Academy of 
Medicine 
 NYS Department of 
Health 
 Healthcore 
 NYU School of 
Medicine HEAL 

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 Lutheran Family Health Center 
Network 
 
*All entities are involved in Activity 1. 
Bolded entities are also a part of Activity 2. 
University 
 
Figure 1: NYC-CDRN Organizational Structure 
 
 
 
 
4) Resources Available to Conduct the Human Research 
Our study team includes multiple experienced investigators, a data analyst, experienced project 
management staff, and an experienced engagement manager. This team has the knowledge and 
resources needed to conduct the retrospective cohort analysis, focus groups, and HH leader 
interviews. ISMMS has robust data management and security protocols in place to safely store study 
data. For this study, we will be working with the NYC-CDRN, which, as described above, has the 
resources to combine data from multiple institutions and facilitate collaboration, and to ensure data 
security and quality. 
 
Activity 2: The primary team at ISMMS will collaborate with partner institutions to conduct  
interviews. Investigators and stakeholders from the partner institutions will advise the primary team 
on reaching out to clinical providers, care management agencies, and HHs about contacting their 
patients. The primary team will reach out to and follow up with these providers.  Then, the primary 
team will reach out to eligible participants. The primary team will conduct all interviews.  
 
 
5) Study Design 
a) Recruitment Methods 
Activity 1: For this retrospective data analysis, we will not be recruiting any participants. The 
study will rely upon a distributed query of the participating healthcare systems in the NYC-
CDRN. For a full description of query design, see section f for Activity 1. The protocol relies 
upon the NYC-CDRN infrastructure established under the BRANY IRB-approved protocol #14-
02-95-380 (PI: Kaushal) to accomplish the data aggregation. 

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Activity 2:  
Health Home patients will be recruited through referral from HH care managers. Care 
managers will share a flyer (created by the study team) describing the study with patients. If 
patients are interested, the care manager will add their contact information to an excel 
spreadsheet that will be shared with study staff. Study staff will follow up via phone. If the 
participant is reached via phone and interested in participating, study staff will provide 
further information about the interview process and content and will schedule the 
phone/virtual interview.  
 
Activity 3: In order to identify leaders from HHs in NYC, we will utilize multiple sources 
including internet sites for each HH listing name and contact information for those in 
leadership positions, referrals from our Patient and Stakeholder Advisory Board (of which one 
member is a leader of a NYC HH and thus familiar with many of these individuals), from 
interviewees who have already participated, and HH forums.  Study staff will reach out to 
individuals by phone or email and describe the study. If potential participants are interested, 
the study staff member will set up a telephone or video meeting, during which verbal consent 
will be obtained prior to any data collection. 
Health Home directors that are study stakeholders and partners will assist with the 
identification of HH care managers and will share contact information of interested 
participants with the study staff in an excel spreadsheet. Study staff will follow up via email 
or via phone. If the participant is reached and interested in participating, study staff will 
provide further information about the interview process and content and will schedule the 
phone/virtual interview.  
 
Activity 4: For this retrospective data analysis, we will not be recruiting any participants We plan to 
use NYS Medicaid administrative and claims data in the Medicaid Data Warehouse, available to 
researchers at HEAL at New York University.  
 
b) Inclusion and Exclusion Criteria 
Activity 1: For this analysis, the study cohort will consist of patients who: 1) have Type II diabetes, 
2) are insured by Medicaid, and 3) are either part of a Health Home or meet criteria for Health Home 
enrollment, but are not enrolled in a Health Home. 
1) In consultation with the Diabetes Subcommittee of the NYC-CDRN, this protocol will use the 
SUPREME-DM definition of diabetes, which has been previously validated.(29) This definition has 
been adopted as a standard across the three CDRNs that have diabetes as a disease focus (New York, 
Louisiana, and Oregon-Advance). It consists of the following algorithm (see Table 1), where any of the 
listed criteria are sufficient for inclusion for any patient with at least one outpatient visit at a 
participating CDRN site within the past year.  
 
Table 1. Diabetes Definition  
Criteria 
Parameter 
ICD9/10 - Inpatient 
discharge diagnosis 
1 or more of (250.x, 357.2, 366.41, 
362.01–362.07) 
 
ICD9/10 - 
Outpatient 
diagnosis 
2 or more of (250.x,357.2, 366.41, 362.01–
362.07) 
Visits must occur on separate days. Ambulatory 
visits only.  

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HbA1c 
2 or more HbA1c's at >=6.5% 
Tests must be on separate days no more than 2 
years apart. 
Fasting Plasma 
Glucose 
2 or more at >= 126 mg/dL 
Tests must be on separate days no more than 2 
years apart. 
Random Plasma 
Glucose 
2 or more at >= 200 mg/dL 
Tests must be on separate days no more than 2 
years apart. 
Random plasma 
plus fasting glucose 
1 at => 200 mg/dL AND 1 at => 126 mg/dL 
Tests must be on separate days no more than 2 
years apart. 
HbA1c plus fasting 
glucose 
1 at >= 6.5% and 1 at >= 126 mg/dL 
Tests can occur on same day but cannot be more 
than 2 years apart. 
HbA1c plus random 
plasma glucose 
1 at >= 6.5% and 1 at >= 200 mg/dL 
Tests can occur on same day but cannot be more 
than 2 years apart. 
2-h 75-g OGTT 
1 at >= 200 mg/dL 
Do NOT count if measured during pregnancy. 
Medications 
Sulfonylurea, insulin, biguanide, SGLT-2 
inhibitors, 
thiazolidinedione, alpha-glucosidase 
inhibitor, 
incretin mimetic, meglitinide, amylin 
analog, or 
dipeptidyl peptidase inhibitor 
>= 1 prescription. If dispense was for metformin, 
any 
thiazolidinedione, or exenatide, at least one lab 
test (HbA1c > 6.5%, random glucose >= 200 mg/dl, 
fasting glucose >= 126 mg/dl) is also required. 
 
2) In order to be eligible for the cohort, subjects need to have been insured by Medicaid 
at some point during the years 2010-2016.  This categorization will be available in the NYC-
CDRN data set. Data will be gathered only from individuals who meet criteria for diabetes and 
have been insured by Medicaid. 
3) Subjects who have participated in the HH program will be the treatment group in this 
study. Medicaid data contains a field that will identify those patients who were a part of this 
program during the study period.  Our comparison group will consist of similar individuals 
who were not part of the HH program. The comparison group will be constructed of patients 
who have been propensity score matched to those in the treatment group, and are therefore 
similar on a number of characteristics. 
 
Activity 2: For the focus group and interview aspect of this study, we will be conducting 2 
rounds of data collection, in years 2 and 5. We aim to recruit 20-40 participants in year 5.  
Year 5 data collection will consist of only individual interviews. Inclusion criteria are as 
follows:  
a. English-speaking 
b. Are enrolled in a HH 
c. 18 years old or older 
All participants must be able to provide informed consent to participate, and must be able to 
speak with a study staff member over the phone/video call.  All individuals must be able to 
communicate in English.  
 
The ISMMS study team will coordinate with stakeholders who lead NYC HHs to engage HH 
members/patients who are potential participants.  
 
 

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Activity 3: HH leaders: In order to participate in this portion of the study, individuals must 
speak English and be in a leadership position at a HH entity in NYC or a NYC HH-affiliated Care 
Management Agency (CMA). Other than not meeting the inclusion criteria, there are no 
exclusion criteria. 
Care managers: In order to participate in this portion of the study, individuals must speak 
English and be in care manager position with a NYC HH-affiliated Care Management Agency 
(CMA). Other than not meeting the inclusion criteria, there are no exclusion criteria. 
 
Activity 4: For this analysis, the study cohort will consist of patients who: 1) have Type II diabetes, 2) 
are insured by Medicaid, and 3) are either part of a Health Home or meet criteria for Health Home 
enrollment, but are not enrolled in a Health Home. These criteria will be determined using only 
Medicaid claims data.  
 
 
 
c) Number of Subjects 
Activity 1: Estimated sample size of 51,000 patients. 
Activity 2: 20-40 participants in year 5. 
Activity 3: Approximately 10-30 participants (HH leadership and CMA leadership, care 
managers). 
Activity 4: Estimated sample size of 60,000 patients 
 
d) Study Timelines 
Activity 1: This is a retrospective cohort analysis. We will examine two data sets: one with 
data from 2007-2014, and one with data from 2010-2017.  The project has been funded from 
3/1/2016-2/28/2021. 
Activity 2: We completed our year 2 focus groups and interviews by 8/31/2018 and we 
anticipate completing year 5focus groups by 2/2021. Participation in the study will consist of 
being a part of an interview, and optional follow up activities (receiving study results). The 
project has been funded from 3/1/2016-2/28/2021. 
Activity 3: We completed our first round of HH leadership interviews by 8/31/18. Year 5 
interviews will be conducted by 2/2021. The project has been funded from 3/1/2016-
2/28/2021.  
Activity 4: We anticipated conducting primary analyses by 6/30/2021.  
 
e) Variable and endpoints 
Activity 1 and 4: For this aspect of the study, we will examine multiple endpoints related to 
process of care, outcomes, and racial/ethnic disparities. We will examine variable and 
endpoints in the following categories: 
1) Individual level characteristics  
Variables include gender, age, race/ethnicity, health insurance type, health care site, patient zip 
code, and tobacco use. 
 
2) Health home indicators and patient co-morbidities 

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Indicators available include whether the individual was enrolled in the HH program, the date of 
joining the program, the name/location of the HH, patient-level co-morbidities and chronic 
conditions (additional diagnoses).  
 
3) Process measures 
In consultation with the Diabetes Subcommittee of the NYC-CDRN, the following process 
measures have been identified (see Table 2). Several organizations in the United States have 
developed sets of quality metrics that can be used to evaluate the adequacy and appropriateness 
of processes of care and outcomes for patients with diabetes. These include the Centers for 
Medicare and Medicaid Services (CMS), the Pharmacy Quality Alliance (PQA), the National 
Committee for Quality Assurance (NCQA)’s Healthcare Effectiveness Data and Information Set 
(HEDIS), and the Agency for Healthcare Research and Quality’s (AHRQ) Prevention Quality 
Indicators (PQI) (which assess the quality of care for “ambulatory care sensitive conditions,” also 
known as preventable admissions).(30-33) 
 
Table 2. Process Measures  
Metric 
Component 
Adherence to Chronic 
Medications (CMS) 
At least two prescriptions for statins and a Proportion of Days Covered (PDC) of at least 
0.8 during the measurement period (12 consecutive months) 
Adherence to Chronic 
Medications (CMS) 
At least two prescriptions for ACEIs/ARBs and a PDC of at least 0.8 during the 
measurement period (12 consecutive months). 
Adherence to Chronic 
Medications (CMS) 
At least two prescriptions for a single oral hypoglycemic agent or at least two 
prescriptions for multiple agents within an antidiabetic class and a PDC of at least 0.8 
for at least 1 antidiabetic class during the measurement period (12 consecutive 
months). 
Eye Exam (NCQA) 
Received a retinal or dilated eye exam during the measurement year or a negative 
retinal or dilated eye exam in the year prior to the measurement year. 
HbA1c testing (NCQA) 
Received an HbA1c test during the measurement year. 
LDL-C Screening 
(NCQA) 
Received an LDL-C test during the measurement year. 
Medical Attention for 
Nephropathy (NCQA) 
Received a nephropathy screening test or had evidence of nephropathy during the 
measurement year. 
Appropriate Treatment 
of Hypertension (PQA) 
If dispensed a medication for diabetes and hypertension, are receiving an angiotensin-
converting -enzyme-inhibitor (ACEI) or angiotensin receptor blocker (ARB) or direct 
renin inhibitor (DRI) renin-angiotensin-antagonist medication. 
Foot exam (NCQA) 
Received a foot exam (visual inspection with either a sensory exam or a pulse exam) 
during the measurement year. 
 
4) Outcome measures  
In consultation with the Diabetes Subcommittee of the NYC-CDRN, the following outcome 
measures have been identified (see table below). As with the identified process measures, these 
are metrics identified and developed by major quality reporting organizations, which means that 
results will be readily interpretable by policymakers and easily compared to other studies and 
datasets.(30) 
 
In order to assess these metrics, and to perform individual-level longitudinal analyses, we will 
evaluate health system utilization and intermediate and long term outcomes. Outcomes will 
include: 
 

12 
 
1) Intermediate outcomes:HbA1c, blood pressure, LDL and renal function 
2) Long-term outcomes: we will use validated ICD-9 code based-algorithms to assess  
a. macrovascular outcomes (stroke and myocardial infarction)  
b. microvascular outcomes (nephropathy, end-stage renal disease, neuropathy, 
amputation, retinopathy, and blindness) 
3) Utilization outcomes:  
a. Number and diagnoses associated with diabetes-related preventable hospitalizations 
i. uncontrolled diabetes admissions 
ii. diabetes short-term complications admissions (diabetic ketoacidosis, 
hyperosmolarity, coma) 
iii. and long-term complications admissions (renal, eye, neurological, and 
circulatory disorders) 
b.  ER visits 
c.  Outpatient visits. 
 
 
Table 3. Outcome measures 
Metric 
HbA1c Control (<8.0%) (NCQA) 
HbA1c Poor Control (>9.0%) (NCQA) 
Blood Pressure Control (<140/90 mm 
Hg) (NCQA) 
Uncontrolled Diabetes Admissions 
Rate (PQI 14) (AHRQ) 
Diabetes Short-Term Complications 
Admissions (PQI 1) (AHRQ) 
Diabetes Long-Term Complications 
Admissions (PQI 3) (AHRQ) 
Lower-Extremity Amputation Among 
Patients With Diabetes (PQI 16) 
(AHRQ) 
Medical Attention for Nephropathy 
(NCQA) 
 
Activity 2: For this qualitative research, we will identify cross-cutting themes from 
interviews. 
Activity 3: Data collected through these interviews will be utilized to categorize HH, CMA, 
and care manager activities and experiences.  
 
f) Procedures Involved in the Human Research 
This is a mixed-methods study that will utilize a retrospective cohort design, as well as 
qualitative focus groups and interviews consisting of a combination of closed- and open-
ended questions.   
Activity 1: This part of the study will examine a merged data set that will include EHR data, 
Medicaid claims data, and HH-specific data. The study will rely upon a distributed query of the 
participating healthcare systems in the NYC-CDRN and HEAL at NYU School of Medicine will 

13 
 
extract the NY State Medicaid data. Our data flow is depicted in the figure and described below. 
The protocol relies upon the NYC-CDRN infrastructure established under the BRANY IRB-
approved protocol #14-02-95-380 (PI: Kaushal) to accomplish the data aggregation.  
1. 
The NYC-CDRN (New York City Clinical Data Research Network) 
conducts a query on centralized, de-identified electronic health record (EHR) data to 
select patients with diabetes.   
2. A Weill Cornell approved vendor (NYC-CDRN Central Repository) identifies the patients 
selected in step 1 who were insured by Medicaid at some point during the study period 
(2007-2017) and provides a list of the relevant CDRN Master IDs and site-specific proxy 
IDs back to the NYC-CDRN. 
3. The NYC-CDRN assigns site- and study-specific IDs (SS-IDs) to each patient. The SS-ID is 
unique to this study. The NYC-CDRN provides a list to: 
4. 4a. The NYC-CDRN central data repository, which holds data from each of the NYC-CDRN 
clinical sites. 
4b. Health Evaluation and Analytics Lab (HEAL)- NYU School of Medicine 
4c. To Icahn School of Medicine at Mount Sinai (ISSMS) (in the form of a mapset, 
containing all linked SS-IDs). 
5. HEAL utilizes SS-IDs to obtain the relevant Medicaid IDs. 
6. HEAL provides Medicaid IDs to NYS Medicaid. 
7. NYS Medicaid pulls HH data for the relevant Medicaid IDs. 
8. NYS Medicaid sends HEAL HH data by Medicaid ID. 
9. NYU HEAL creates a limited data set (containing 5-digit zip code and actual dates) 
including Medicaid claims data and HH data by SS-IDs. 
10. HEAL sends limited data set to Mount Sinai. 
11. The NYC-CDRN central data repository, which holds data from each NYC-CDRN clinical 
site, uses the SS-IDs provided to them in step 4a to perform a  query of EHR data and 
creates a limited data set (containing 5-digit zip code and actual dates). 
12. The NYC-CDRN central data repository, which holds data from each NYC-CDRN clinical 
site, provides limited data set with SS-IDs to ISMMS. 
13. ISMSS merges Medicaid and HH data with EHR limited data sets using the map set of SS-
IDs.  
This merged data set will then be used of analyses. 
We will perform this process twice: once in 2017 for data 2007-2014 and once in 2019, for data 
2010-2017. 
We will perform this process twice: once in 2017 for data 2007-2014 and once in 2019, 
for data 2010-2017. 
Activity 2: Interviews will be conducted with 20-40 participants in year 5. Given the 
COVID-19 pandemic, all interviews will be conducted remotely, either by phone or video 
call. One-two staff members will be present to obtain consent, conduct the interview, 
take notes, and facilitate recording. The interview guide was developed with the input of 
our study’s investigators and Patient/Stakeholder Advisory Board (PSAB).  The topics 
covered include: the experiences of HH members/clients/patients in NYC during the 
COVID-19 pandemic, particularly with regards to accessing care (for COVID-19 and non-
COVID conditions) and accessing social services. 

14 
 
Based on experiences with initial  interviews, we will iteratively modify the guide based 
on discussion flow and content. 
No linkage with or examination of the participant’s medical record will occur. Transcripts 
will be stripped of identifying information and not linked in any way to PHI. 
Activity 3: One-on-one interviews will be conducted with leaders from NYC HHs and care 
managers.  No other procedures will be performed with participants. The interviews will 
cover topics including: HH and Care Management Agency (CMA) structure, activities, 
reporting, quality measurement, quality improvement, challenges, areas of excellence, 
referral and enrollment processes, characteristics of the population served, and the 
experiences of New Yorkers working with the Health Home (care managers, program 
leaders) during the COVID-19 pandemic.  The interview guide was developed in 
collaboration with our study’s Patient/Stakeholder Advisory Board (PSAB). These 
interviews are low-risk: they do not touch on private or confidential information. 
 
Activity 4: We plan to use NYS Medicaid administrative and claims data in the Medicaid Data 
Warehouse, available to researchers at HEAL at New York University. This is the same data set described 
in Activity 1.  
 
 
g) Specimen Banking 
Not applicable. 
 
h) Data Management and Confidentiality 
Activity 1: The NYC-CDRN has developed standardized protocols to transfer data securely 
between sites (including ISMMS), RHIOs, a Weill Cornell approved vendor (NYC-CDRN Central 
Repository), Project Ops of the NYC-CDRN. All data flows between parties will use established, 
secure methods of transmission.  These currently include secure FTP and public key encryption 
protocol (for data transmission from sites to a Weill Cornell approved vendor); secure FTP (for 
data transmission from sites to the RHIOs); secure sockets layer (for data transmission from 
Healthix to a Weill Cornell approved vendor).  The data flow was created to minimize any privacy 
and confidentiality risks through the use of Member IDs (proxy IDs) and of Site- and Study-
Specific IDs (SS-IDs).   
Dr. Mayer and her team have signed a contract committing them to comply with NYC-CDRN 
privacy and security policies and abide by applicable state and federal privacy and security legal 
requirements.  
Dr. Mayer will upload all data received to a secured, password-protected, fire-walled local hard 
drive. Only Dr. Mayer and other authorized members of the analysis team will be able to access 
these files for the purpose of conducting analyses.  All authorized members of the team will have 
completed any required CITI courses in biomedical research.  After all analyses are complete, the 
files will be removed from the hard drive and deleted permanently.   
Activity 2: Focus groups, telephone focus groups, and individual interviews will be audio 
recorded. Audio recordings will be securely shared with a HIPAA compliant transcription 
and translation company. Focus group and interview transcripts will be stripped of 
identifiers prior to storage on password-protected, hard-drives.  Participant contact 
information will be stored separately so that data cannot be linked with the participant 

15 
 
(no study ID or other link will be used).  Contact information will also be stored only on 
password-protected hard drives. Only authorized study staff who have completed 
research ethics training will have access to any data. Data will be stored for an additional 
year after all analyses have been completed. 
Activity 3: Contact data for this aspect of the study is publicly available for HH and CMA 
leaders. All contact data for leaders and care managers will be stored on password-
protected hard drives.   Interviews will be audio recorded and transcribed and transcripts 
will be stripped of identifiers prior to storage on password-protected, hard-drives.  
Participant contact information will be stored separately so that data cannot be linked 
with the participant (no study ID or other link will be used).  Only authorized study staff 
who have completed research ethics training will have access to any data. Data will be 
stored for an additional year after all analyses have been completed. 
 
i) Provisions to Monitor the Data to Ensure the Safety of Subjects 
This research involves no more than minimal risk to subjects. The PI, Dr. Mayer, will 
oversee data monitoring, focusing on the protection of the confidentiality of participant 
data. The Principal Investigator, Dr. Victoria Mayer, and the primary study team are 
located at the Icahn School of Medicine at Mount Sinai (ISMMS). The study will utilize the 
IRB of the Biomedical Research Alliance of New York (BRANY) and the Columbia 
University IRB (the only member of the New York City Clinical Data Research Network 
(NYC-CDRN) that does not rely on BRANY).  
 
The PI, Dr. Mayer, will monitor the research study in close collaboration with and in 
concert with the regulations of the staff of the NYC-CDRN, NYS Medicaid’s Data 
Warehouse Office of Health Insurance Programs Privacy Office, BRANY, the Columbia 
University IRB, and the ISMMS.  
 
This study will utilize multiple data sources, including 1) data from the NYC-CDRN and 
data from New York State Medicaid (both retrospective), 2) focus groups and interviews, 
and 3) one-on-one interviews with Health Home leaders. All 3 aspects of this study 
present no more than minimal risk to study participants. The NYC-CDRN has developed a 
comprehensive set of privacy and security policies and procedures that include strong 
provisions to protect data security at rest and in motion in line with state and federal law 
and industry best practices.  All sites have signed a master data use agreement, and 
investigators are required to sign individual data use agreements, agreeing to comply 
with those terms. Focus group data and interview data will be stored securely at ISMMS 
on password-protected, firewalled hard drives.  The principle research-associated risk for 
the main retrospective cohort analysis is that of breach of confidential data. In order to 
minimize this risk, we will work with the NYC-CDRN and NYS Medicaid to ensure that all 
data transfer and storage protocols are consistent with best practices for data security. 
All data at ISMMS will be stored and accessed according to established regulations of 
ISMMS to ensure data privacy and security. Limited data sets will be constructed to 
contain the minimal amount of PHI needed to complete the study.  

16 
 
 
While we do not anticipate adverse events associated with our focus groups or 
interviews, in the case of an adverse event the study team would report this to BRANY for 
review. In the case of unanticipated problems, the study team will report this to BRANY a 
for review.  
 
 
j) Withdrawal of Subjects 
Activity 1 and 4: Researchers will not be able to identify subjects. Identifying subjects in 
order to withdraw them from the study would compromise confidentiality.   
Activity 2: Subjects may withdraw from the study at any time by contacting the study PI 
or Engagement Manager.  If subjects choose to withdraw from the study, we will not 
collect any further data, but will utilize already collected data as part of the focus group 
or individual interview. 
Activity 3: Subjects may withdraw from the study at any time by contacting the study PI 
or Engagement Coordinator.  If subjects choose to withdraw from the study, we will not 
collect any further data, but will utilize already collected data. 
 
 
 
6) Risks to Subjects 
Activity 1 and 4: The principal potential risks to subjects are confidentiality risks. We 
believe the risk is minimal because researchers, partner sites, and the a Weill Cornell 
approved vendor (NYC-CDRN Central Repository) must follow rigorous, established NYC-
CDRN policies and procedures for privacy and security. NYC-CDRN project staff are 
trained in privacy and security, data transfer protocols are in place and data will be held 
on servers approved for holding PHI. The limited data sets that contain retrospective EHR 
data and Medicaid data, which will be given by the various NYC-CDRN partner sites to 
investigators at Mount Sinai will meet all applicable federal regulations and there is 
minimal risk to patient confidentiality. The merged limited data sets will be stored on 
servers approved for holding PHI. Only study staff trained in data privacy, security, and 
research ethics will be able to access this data.  
Activity 2: The principal potential risks to subjects are non-physical privacy and 
confidentiality risks. As far as confidentiality, transcripts will be stripped of all identifying 
information during the transcription process. We will use a HIPAA compliant transcription 
service.  Thus, transcripts used for analysis will not contain identifiable data. Any sharing 
of results will also not contain any identifiable information. In addition, participants may 
find it uncomfortable to answer certain questions that come up during the interviews. 
Moderators will make it clear that participants can stop sharing at any time, and that they 
do not need to answer any questions that make them feel uncomfortable- it is their 
choice.  
Activity 3: There is a minimal confidentiality risk associated with the HH leader and care 
manager interviews. It is unlikely that any information shared in these interviews will be 
confidential; however, participants may share sensitive opinions/criticisms. When 
reporting results, we will therefore remove identifying information by which the 
interviewee could be surmised.  

17 
 
 
7) Provisions for Research Related Harm/Injury 
This research involves no more than minimal risk to subjects.  
 
8) Potential Benefits to Subjects 
Activity 1 and 4: There will be no direct benefit to subjects whose data is a part of this 
study. The overall goal of the study is to evaluate the impact of the Health Home 
intervention on patients with diabetes. Identifying care management programs that are 
effective can offer benefit to the population of patients with diabetes and complex 
medical problems. 
Activity 2: There will be no direct benefit to focus group or individual interview 
participants. As above, the overall study aims to benefit patients with diabetes. 
Activity 3: There will be no direct benefit to HH leaders or care managers who participate 
in interviews. However, as key stakeholders in the HH program, they may benefit from 
knowledge eventually produced by the study. 
 
9) Provisions to Protect the Privacy Interests of Subjects 
Activity 1 and 4: This aspect of the research involves retrospective data analysis, with no 
contact with participants.  
Activity 2:  Interview recruitment: HH care managers will share the names and contact 
information of interested members/clients/patients with the recruitment team, or 
interested participants can reach out directly to team staff. The recruitment team will be 
trained in responsible research practices and will not reveal any private information via 
voicemail or message.  No private information will be elicited by phone.  
Interviews: We will let participants know that they may decline to answer any questions 
or refrain from participating in any discussions that are not comfortable or which raise 
privacy concerns for them.  Any sharing of results will also not contain any identifiable 
information.   
Follow up:  As part of the study, we have proposed sharing overall study findings with 
participants from focus groups and interviews. Thus, we have included in the consent 
form that we would like to contact participants in the future regarding study findings.  In 
addition, we will utilize address information to send gift cards to participants. Again, all 
outreach staff will not disclose private information as part of outreach efforts. 
Activity 3: Information on the names and contact information of HH leaders is publicly 
available, and we will not be risking privacy by recruiting these participants. Care manager 
information will not be linked with responses. We will make it clear to those who choose 
to participate that data on their personal opinions/criticisms will not be linked with their 
identity/specific-HH.  
 
10) Economic Impact on Subjects 
Activity 1: no costs to subjects. 
Activity 2: no costs to subjects. 
Activity 3: no costs to subjects. 
Activity 4: no costs to subjects. 
 
 

18 
11) Payments to Subjects
Activity 1 and 4: There will be no payments to subjects. 
Activity 2: We will provide a $40 gift card to each participant in appreciation of their time, 
individuals who participate in individual phone interviews will receive their gift cards in 
the mail.  
Activity 3: We will provide a $100 gift card to each participant in appreciation of their 
time. 
12) Consent Process
Activity 1 and 4: For this analysis, we request a HIPAA waiver. 
Activity 2: Prior to participation in an individual phone interview,  study staff will read a 
script describing the research study, the risks and benefits of participating, and requesting 
verbal consent. Participants will be provided with written information describing the 
research. We are seeking a waiver of written consent in this case. The only purpose of an 
in-person interaction would be to obtain written consent. This aspect of the research 
presents no more than minimal risk of harm to subjects, and this research involves no 
procedures for which written consent is normally required outside of the research 
context. 
Activity 3: We request a waiver of written documentation of the consent process. This 
aspect of the research presents no more than minimal risk of harm to subjects, and this 
research involves no procedures for which written consent is normally required outside of 
the research context. Study staff will read a script describing the research study, the risks 
and benefits of participating, and requesting verbal consent by phone. Participants will be 
provided with written information describing the research. 
13) Process to Document Consent in Writing
Activity 1 and 4: For this analysis, we request a HIPAA waiver. 
Activity 2: Any individuals who participate in individual phone interviews will provide 
verbal consent before the interview begins. 
Activity 3: We request a waiver of written documentation of the consent process. This 
aspect of the research presents no more than minimal risk of harm to subjects, and this 
research involves no procedures for which written consent is normally required outside of 
the research context.  
14) Vulnerable Populations
Activity 1 and 4: This retrospective data analysis will not specifically target vulnerable 
populations; however, vulnerable individuals may be a part of the cohort. For this arm of the 
research, we will not be asking participants to engage in research-related risk, and will have 
measures in place to protect the confidentiality of all individuals who are included in the 
research.  
Activity 2: For the interviews, we will not recruit individuals from vulnerable populations. All 
participants will need to be able to give consent and be adults.  

19 
Activity 3: The HH-leader and care manager interviews will be carried out with individuals in 
leadership positions, and we will not recruit individuals from specific vulnerable populations. 
15) Multi-Site Human Research (Coordinating Center)
This is a multi-site study with ISMMS as the lead entity. All data will be stored at ISMMS. 
Investigators from other institutions will have access only to data from analyses, but not 
to individual level data. They will participate in developing analysis plans and reviewing 
results.   
16) Sharing of Results with Subjects
Results of all activities will be shared in aggregate through academic presentations and 
publications, lay talks, lay press communications. Results of overall study will be 
disseminated among focus group and interview participants.  
17) Control of Drugs, Biologics, or Devices
Not applicable. 
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