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Premature Heart Disease Mortality in
Native Americans (PHDMNA) Study
Miranda Freeman
UT Health Science Center School of Public Health, Austin Regional Campus
4/30/2014

MOP Section 1: Brief Study Synopsis

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CVD Risk Factors and Premature Heart Disease Mortality among Native
Americans (45-64 years of age) living in North and South Dakota (2010-2013)
Until fairly recently, it was widely believed that the rate of cardiovascular disease
(CVD) was lower among American Indians and Alaskan Natives (AIAN) than in the
general US population (6). However, along with CVD being their leading cause of
death, American Indians have high prevalence rates of several major CVD risk factors,
including type 2 diabetes, obesity, hypertension, high cholesterol, and smoking (6, 7).
In fact, 63.7% of AIAN men and 61.4% of AIAN women have been found to possess
one or more of these risk factors (1). Later studies have shown that total CVD mortality
for AIAN is actually higher than the national average and assert that racial
misclassification is to blame for the error (6). It was found that on average, AIAN race
was misidentified 10.9% of the time and that the death rates were underestimated by
almost 21% resulting in unreasonably low estimates of CVD. When data was adjusted
for the misclassification, the heart disease mortality rate was found to be 157.1 per
100,000 for AIAN compared to 130.5 per 100,000 for the general US population (6).
Furthermore, while the US CVD mortality rates were shown to be slowly decreasing
when all races were taken into account, the rates specific to AIAN were shown to be
increasing, thereby further increasing the disparity (6).
This increase in CVD mortality may be due in part to the growing rates of
diabetes among American Indians. The Strong Heart Study, a longitudinal study of
CVD and its risk factors among various American Indian communities, found a 48%
prevalence of type 2 diabetes among American Indians aged 45 to 64 years of age
compared to a prevalence of 5.5% in the general US population of the same age group
(4, 8). Furthermore, in the years of follow-up, 56% and 78% of CVD events occurred in
men and women with diabetes respectively (3). American Indians younger than 45
years of age that were diagnosed as having type 2 diabetes were significantly more
likely to carry several modifiable CVD risk factors (i.e. they were more likely to be
obese, smoke tobacco, and have high glycated hemoglobin (HbA1c) values) compared
to those older than 45 (7). Further data was presented showing that type 2 diabetes is
strongly associated with CVD. Compared to non-diabetic American Indians, diabetic

AIAN men were found to be at a 2.2 times greater risk of CVD while diabetic AIAN
women were found to have a 3.5 times increased risk of CVD (3).
Of particular interest though, is the high incidence of premature heart disease
mortality among AIAN populations. While diseases of the heart are the leading cause of
death starting at 45 years of age for AIAN individuals, the same cannot be said about
the general population. For the US as a whole, diseases of the heart do not become
the leading cause of death until 65 years of age (6). It has been documented that AIAN
have the highest proportion of premature death (defined as death at <65 years of age)
from heart disease (36%) in the United States. In comparison, whites (14.7%), blacks
(31.5%), and the general population (16.5%) in the US have considerably fewer
incidences of premature heart disease mortality (2). More studies need to be done to
explore this phenomenon and examine its implications on the future.
Proposal and Public Health Significance
Given the large health disparities between the US general population and AIAN,
as well as the increasing CVD mortality rates among American Indians, more work
needs to be done to better understand the reasons behind such high rates in order to
formulate ways to amend the problem. The high incidence of premature heart disease
mortality among AIAN populations is of particular concern, however, little information
has actually been collected to try and assess why these rates are so high. A study
needs to be done to examine what exposures are related to the incidence of premature
heart disease mortality. The exposures of primary interest are CVD risk factors
including obesity, hypertension, high cholesterol, smoking, and especially type 2
diabetes due to its high prevalence among AIAN and also because it has been shown to
be strongly associated with CVD (3). As for the study population, because American
Indian populations are not homogenous and there are important regional as well as
tribal differences in CVD rates and risk factors, the study of a single area may be more
conducive to determining measures of association and preventative actions or programs
that may benefit the community. The Strong Heart Study found that the Sioux American
Indians of North and South Dakota had the highest rates of coronary heart disease, with
incidence rates of 40.2 per 1000 person-years for individuals with diabetes and 17.7 per

1000 person-years for individuals without diabetes (5). Focusing on this population may
help better understand the associations between CVD risk factors and premature heart
disease mortality. The overall goal of this study is to examine the impact of various
cardiovascular disease (CVD) risk factors (obesity, hypertension, LDL cholesterol,
albuminuria, smoking, and type 2 diabetes) on premature heart disease mortality
(defined as death at <65 years of age) in Native American men and women (45-64
years of age), living in North and South Dakota from 2010 - 2013. Potential
confounders that would need to be controlled for would include socioeconomic status,
sex, alcohol consumption, diet, physical activity, menopause status, number of
pregnancies, and hormone replacement therapy. Family history may be an effect
modifier.
References
1. Centers for Disease Control and Prevention (2000). Prevalence of selected
cardiovascular disease risk factors among American Indians and Alaska Natives-
United States, 1997. MMWR, 49(21), 461–465.
2. Galloway, J. M. (2005). Cardiovascular health among American Indians and
Alaska Natives: successes, challenges, and potentials. American Journal of
Preventive Medicine, 29(5), 11-17.
3. Howard, B. V., Lee, E. T., Cowan, L. D., Devereux, R. B., Galloway, J. M., Go, O.
T., ... & Welty, T. K. (1999). Rising tide of cardiovascular disease in American
Indians: the Strong Heart Study. Circulation, 99(18), 2389-2395.
4. Howard, B. V., Welty, T. K., Fabsitz, R. R., Cowan, L. D., Oopik, A. J., Le, N. A.,
... & Lee, E. T. (1992). Risk factors for coronary heart disease in diabetic and
nondiabetic Native Americans: the Strong Heart Study. Diabetes, 41(Supplement
2), 4-11.

5. Xu, J., Lee, E. T., Peterson, L. E., Devereux, R. B., Rhoades, E. R., Umans, J.
G., ... & Howard, B. V. (2012). Differences in Risk Factors for Coronary Heart
Disease among Diabetic and Nondiabetic Individuals from a Population with High
Rates of Diabetes: The Strong Heart Study. Journal of Clinical Endocrinology &
Metabolism, 97(10), 3766-3774.
6. Rhoades, D. A. (2005). Racial misclassification and disparities in cardiovascular
disease among American Indians and Alaska Natives. Circulation, 111(10),
1250-1256.
7. Rith-Najarian, S. J., Gohdes, D. M., Shields, R., Skipper, B., Moore, K. R.,
Tolbert, B., ... & Acton, K. J. (2002). Regional variation in cardiovascular disease
risk factors among American Indians and Alaska Natives with diabetes. Diabetes
Care, 25(2), 279-283.
8. Yusuf, S., Reddy, S., Ôunpuu, S., & Anand, S. (2001). Global burden of
cardiovascular diseases Part II: Variations in cardiovascular disease by specific
ethnic groups and geographic regions and prevention strategies. Circulation,
104(23), 2855-2864.

MOP Section 2: Research Objectives

Study Goal and Research Objectives
The overall goal of this study is to examine the impact of various cardiovascular disease
(CVD) risk factors (obesity, hypertension, LDL cholesterol, albuminuria, smoking, and
type 2 diabetes) on premature heart disease mortality (defined as death at <65 years of
age) in Native American men and women (45-64 years of age), living in North and
South Dakota from 2010 - 2013.
Research Objectives:
1. To assess the prevalence of various CVD risk factors among individuals that died
prematurely due to heart disease.
2. To assess the prevalence of various CVD risk factors among individuals that
survived to be >65 years of age.
3. To examine the association between CVD risk factors and premature heart
disease mortality controlling for confounders (such as socioeconomic status and
diet).

MOP Section 3: Study Design

Study Design
The Strong Heart Study (SHS) has been prospectively following American Indian
individuals since 1988 and has consistently found strong associations between
cardiovascular disease (CVD) and several of its risk factors. Using Cox regression
analysis, type 2 diabetes, albuminuria (high levels of albumin in the urine), and
hypertension were found to be significantly associated with CVD in both sexes, while
LDL cholesterol levels were only found to be significantly associated with CVD in men.
Neither smoking nor obesity demonstrated a strong association in the cohort (3, 4, 5).
However, similar analyses have not been done to examine the relationship between
these CVD risk factors and premature heart disease mortality (defined as death at <65
years of age). For these reasons, a nested case-control study will be done to examine
the association between CVD risk factors and premature heart disease mortality within
the SHS prospective cohort (as this is the largest source of information on American
Indians to date) with type 2 diabetes, albuminuria, hypertension, LDL cholesterol,
smoking, and obesity as the primary exposures of interest.
The nested case-control study design is ideal for examining diseases with a long
incubation period, like CVD, and is capable of examining multiple exposures. It also
helps reduce recall bias which is especially important here since information on some
risk factors cannot be gathered from the already deceased. However, it does have
some limitations. As recruitment for the SHS was largely carried out by local community
members, the study may be subject to selection bias, in which case participants were
more involved with the community or visited the doctor more frequently (4). Also, as the
data are generated from a cohort, attrition and maturation (in which individuals develop
a healthier lifestyle after learning that they possess CVD risk factor(s)) may be a
problem. Furthermore, the restriction of the study participants to American Indians aged
45-64, living in North and South Dakota from 2010-2013 limits the external validity of the
study since the results can’t be generalized. However, the results may be applicable to
similar American Indian populations, and the sample restrictions could help control
confounding. Stratification or multivariate analysis can be used to further limit the
effects of confounding in the analysis stage of the study.

References
2), 4-11.
Metabolism, 97(10), 3766-3774.
1250-1256.

Care, 25(2), 279-283.
104(23), 2855-2864.

MOP Section 4: Ethical Considerations

COLLABORATIVE INSTITUTIONAL TRAINING INITIATIVE (CITI)
HUMAN RESEARCH CURRICULUM COMPLETION REPORT
Printed on 02/16/2014
LEARNER Miranda Freeman (ID: 4026343)
PHONE 512.293.0948
EMAIL miranda.j.freeman@uth.tmc.edu
INSTITUTION University of Texas Health Science Center at Houston
EXPIRATION DATE 02/15/2017
GROUP 2 SOCIAL AND BEHAVIORAL RESEARCHERS AND KEY PERSONNEL
COURSE/STAGE: Basic Course/1
PASSED ON: 02/16/2014
REFERENCE ID: 12369413
REQUIRED MODULES DATE COMPLETED
Avoiding Group Harms - U.S. Research Perspectives 02/14/14
Belmont Report and CITI Course Introduction 02/14/14
History and Ethical Principles - SBE 02/14/14
Basic Institutional Review Board (IRB) Regulations and Review Process 02/14/14
Informed Consent - SBE 02/14/14
Records-Based Research 02/14/14
Research With Protected Populations - Vulnerable Subjects: An Overview 02/15/14
Research with Children - SBE 02/15/14
Vulnerable Subjects - Research Involving Pregnant Women, Human Fetuses, and Neonates 02/15/14
Internet Research - SBE 02/15/14
Research and HIPAA Privacy Protections 02/15/14
Conflicts of Interest in Research Involving Human Subjects 02/16/14
University of Texas Health Science Center at Houston 02/16/14
For this Completion Report to be valid, the learner listed above must be affiliated with a CITI Program participating institution or be a paid
Independent Learner. Falsified information and unauthorized use of the CITI Progam course site is unethical, and may be considered
research misconduct by your institution.
Paul Braunschweiger Ph.D.
Professor, University of Miami
Director Office of Research Education
CITI Program Course Coordinator
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INFORMED CONSENT FORM TO TAKE PART IN RESEARCH
Premature Heart Disease Mortality in Native Americans (PHDMNA)
HSC-XX-XX-XXXX
You are invited to take part in a research project called Premature Heart Disease Mortality in Native Americans
(PHDMNA), conducted by Miranda Freeman, of the University of Texas Health Science Center. For this research project,
she will be called the Principal Investigator or PI.
Your decision to take part is voluntary. You may refuse to take part or choose to stop from taking part, at any time. A
decision not to take part or to stop being a part of the research project will not change the services available to you
through the University of Texas Health Science Center or the Strong Heart Study.
You may refuse to answer any questions asked or written on any forms. This research project has been reviewed by the
Committee for the Protection of Human Subjects (CPHS) of the University of Texas Health Science Center at Houston as
HSC-XX-XX-XXXX.
The purpose of this research study is to examine the effect that different cardiovascular disease (CVD) risk factors
(including obesity, high blood pressure, high cholesterol, smoking, and type 2 diabetes) have on the event of premature
death from heart disease (defined as death before 65 years of age) in Native American men and women living in North
and South Dakota.
This is a local study with 3 locations across North and South Dakota. Approximately 832 people will be enrolled in the
study.
If you agree to take part in this study, the data that was collected previously between the years 2010 and 2013 as part of
the Strong Heart Study will be examined to measure the existence of any associations between the risk factors
mentioned previously (i.e. obesity, high blood pressure, etc.) and premature death due to heart disease. The data that
will be looked at will include data obtained from previous clinical exams (including weight, waist to hip measurements,
blood pressure, cholesterol levels, blood tests, urine tests, and ECG results). Data will also be gathered on your gender,
socioeconomic status, family history of heart disease, diagnosis with type 2 diabetes, level of alcohol consumption, the
INVITATION TO TAKE PART
PURPOSE
PROCEDURES

use of cigarettes, number of pregnancies and menopause status (if applicable), the use of hormone replacement
therapy, and diet. Age at time of death due to heart disease will also be examined (if applicable).
You will not be asked to invest any time into this research study since all the data has already been collected.
The results of the study may benefit future generations of Native Americans living in North and South Dakota as well as
those living in other regions. Information gained from the study may help develop new interventions to improve Native
Americans’ health and decrease the number of deaths due to heart disease.
This study does not include any physical risks. However, there is always the possible risk of breach of confidentiality.
The study may involve other risks that are unforeseeable at this time, such as potential psychological, legal, and social
risks upon release of the study results.
The only alternative is not to take part in this study.
Your decision to take part is voluntary. You may decide to stop taking part in the study at any time. A decision not to
take part or to stop being a part of the research project will not change the services available to you through the
University of Texas Health Science Center or the Strong Heart Study.
Also, there may be instances where the PI may withdraw you from the research study. This may occur if you do not later
consent to future changes that are made in the study plan, if the study is stopped by the sponsor ahead of schedule, or
for any other reason.
Information about you will no longer be used if you decide to withdraw yourself from the study.
TIME COMMITMENT
BENEFITS
RISKS AND/OR DISCOMFORTS
ALTERNATIVES
STUDY WITHDRAWAL

If you decide to take part in this research study, you will not incur any additional costs.
You will not be paid for taking part in this study.
You will not be personally identified in any reports or publications that may result from this study. Any personal
information about you that is gathered during this study will remain confidential to every extent of the law. A special
number (code) will be used to identify you in the study and only the investigator will know your name. There is a
separate section in this consent form that you will be asked to sign which details the use and disclosure of your
protected health information.
Once the study is complete, the final results of the study will be sent to you via email.
If you have questions at any time about this research study, please feel free to contact the PI, Miranda Freeman, at (512)
888-8888, as she will be glad to answer your questions. You can contact the study team to discuss problems, voice
concerns, obtain information, and offer input in addition to asking questions about the research.
COSTS, REIMBURSEMENT AND COMPENSATION
e included:
CONFIDENTIALITY
NEW INFORMATION
QUESTIONS

AUTHORIZATION TO USE AND DISCLOSE
PROTECTED HEALTH INFORMATION FOR RESEARCH
Patient Name:_________________________________ Date of birth:___________________
Protocol Number and Title: HSC-XX-XX-XXXX
Premature Heart Disease Mortality in Native Americans (PHDMNA)
Principal Investigator: Miranda Freeman
If you sign this document, you give permission to The University of Texas Health Science Center at Houston AND/OR
Memorial Hermann Healthcare System to use or disclose (release) your health information that identifies you for the
research study named above.
If you sign this document, you give permission to the researchers to obtain health information from the following
providers:
Name of Provider Address of Provider Fax Number of Provider
The Strong Heart Study
Strong Heart Study Coordinating
Center
Center for American Indian Health
Research
College of Public Health
P.O. Box 26901
Oklahoma City, OK 73190
The health information that we may use or disclose (release) for this research includes information in a medical record,
results of physical examinations, medical history, lab tests, and certain health information relating to heart disease.

The health information listed above may be used by and/or disclosed (released) to researchers and their staff. The
researchers may disclose information to employees at The University of Texas Health Science Center at Houston
AND/OR Memorial Hermann Healthcare System for the purposes of verifying research records. The researchers may also
disclose information to the following entities:
 Sponsor (name sponsor/CRO if applicable)
 Food and Drug Administration
 Data Safety Monitoring Board
The University of Texas Health Science Center at Houston AND/OR Memorial Hermann Healthcare System is required by
law to protect your health information. By signing this document, you authorize The University of Texas Health Science
Center at Houston AND/OR Memorial Hermann Healthcare System to use and/or disclose (release) your health
information for this research. Those persons who receive your health information may not be required by Federal
privacy laws (such as the Privacy Rule) to protect it and may share your information with others without your
permission, if permitted by laws governing them.
If all information that does or can identify you is removed from your health information, the remaining information will
no longer be subject to this authorization and may be used or disclosed for other purposes. No publication or public
presentation about the research described above will reveal your identity without another authorization from you.
Please note that health information used and disclosed may include information relating to HIV infection; treatment for
or history of drug or alcohol abuse; or mental or behavioral health or psychiatric care.
Please note that you do not have to sign this Authorization. University of Texas Health Science Center AND/OR Memorial
Hermann Healthcare System may not withhold treatment or refuse treating you if you do not sign this Authorization.
You may change your mind and revoke (take back) this Authorization at any time. Even if you revoke this Authorization,
researchers may still use or disclose health information they already have obtained as necessary to maintain the
integrity or reliability of the current research. To revoke this Authorization, you must write to:
Miranda Freeman
The University of Texas Health Science Center at Houston
1616 Guadalupe, Suite 6.300
Texas 78701
Fax: 512-888-8888
Privacy Officer
Memorial Hermann Healthcare System
909 Frostwood
Texas 77074
Fax: 713-338-4542
This Authorization will expire 6 years after the end of the study.

SIGNATURES
Sign below only if you understand the information given to you about the research and you choose to take part. Make
sure that any questions have been answered and that you understand the study. If you have any questions or concerns
about your rights as a research subject, call the Committee for the Protection of Human Subjects at (713) 500-7943. You
may also call the Committee if you wish to discuss problems, concerns, and questions; obtain information about the
research; and offer input about current or past participation in a research study. If you decide to take part in this
research study, a copy of this signed consent form will be given to you.
Printed Name of Subject Signature of Subject Date/Time
Printed Name of
Person Obtaining Consent
Signature of
Person Obtaining Consent
Date/Time
CPHS STATEMENT: This study (HSC-XX-XX-XXXX) has been reviewed by the Committee for the Protection of Human
Subjects (CPHS) of the University of Texas Health Science Center at Houston. For any questions about research subject's
rights, or to report a research-related injury, call the CPHS at (713) 500-7943.

MOP Section 5: Operational Objectives and Flowchart

Operational Objectives
I. Study Preparation / Before Entering the Field
1. To obtain IRB approval to conduct the study.
2. To obtain consent to extract the data collected by the Strong Heart Study
(SHS).
3. To recruit personnel.
4. To train staff to understand HIPAA laws.
5. To train staff to understand the research and operational objectives.
6. To train staff about the details of data extraction and management.
II. In the Field
7. To develop a sampling frame.
8. To prepare a list of eligible participants within the SHS database.
9. To use stratified random sampling to select a sample of 208 participants
from the SHS database to serve as cases (individuals that died
prematurely due to cardiovascular disease (CVD) at <65 years of age).
10.To use stratified random sampling to select a sample of 624 participants
from the SHS database to serve as controls (individuals that survived to
be >65 years of age).
11.To obtain informed consent for the use of the information collected by the
SHS from selected participants or their surviving family members.
12.To extract data from the SHS database based on the selected samples.
III. Office Duties
13.To develop a database to safely store data and ensure its security.
14.To develop a system of data management.
15.To develop a system for data quality control.
16.To enter the collected data into a secure database.
17.To examine the data for quality control purposes and correct any errors.
18.To analyze the data and assess the prevalence of CVD risk factors
(including obesity, hypertension, LDL cholesterol, albuminuria, smoking,
and type 2 diabetes) among individuals that died prematurely due to heart
disease (the cases).

19.To analyze the data to assess the prevalence of CVD risk factors
(including obesity, hypertension, LDL cholesterol, albuminuria, smoking,
and type 2 diabetes) among individuals that survived to be >65 years of
age (the controls).
20.To calculate odds ratios to measure the association between CVD risk
factors and premature heart disease mortality controlling for confounders.
IV. Data Reporting
21.To prepare a report discussing any significant measures of association
that were discovered.

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Flowchart:

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MOP Section 6: Sampling Methods, Estimating
Sample Size, and Computing Statistical Power

Estimating Sample Size
Alpha = 0.01 Alpha = 0.05
Power 0.80 0.85 0.90 0.80 0.85 0.90
Sample
Size
580 652 748 392 452 532
The study will consist of independent cases and controls with 3 controls per
case. Type 2 diabetes is the primary exposure of interest due to its high prevalence
among American Indians and Alaskan Natives (AIAN) and because it has been shown
to be the strongest determinant of cardiovascular disease (CVD) (3). Prior literature has
presented data showing that approximately 25% of AIAN men and 38% of AIAN women
living in North and South Dakota have type 2 diabetes (4). In order to be conservative
in the calculations, 0.25 was used as the probability of exposure among controls (p0).
Previous research has shown that diabetic AIAN men have a 2.2 times greater risk of
CVD, while diabetic AIAN women have a 3.5 times increased risk of CVD (3). Data on
premature heart disease mortality is very limited, so this information will be used in
order to study the association between type 2 diabetes, along with other CVD risk
factors, and this health outcome. Thus, a conservative odds ratio of 2 was used in
making the calculations. If the true odds ratio for premature heart disease mortality in
diabetic subjects relative to non-diabetic subjects (ψ) is in fact 2, then 133 case patients
and 399 control patients will need to be studied to be able to reject the null hypothesis
that this odds ratio equals 1 with probability (power) 0.9. The Type I error probability
associated with this test is 0.05. This sample was chosen to ensure that if there is an
association between type 2 diabetes and premature heart disease mortality, there will
be a large enough sample and sufficient power to support it, while still being
conservative in the number of individuals that are used in the study. The smaller
calculated sample sizes may not have had enough power to do this, while others were
much too large in size and would be unethical. An uncorrected chi-squared statistic will
later be used to evaluate the previously mentioned null hypothesis.

References
2), 4-11.
Metabolism, 97(10), 3766-3774.
1250-1256.

Care, 25(2), 279-283.
104(23), 2855-2864.

Sampling Methods
 Sampling frame:
Native Americans living in North and South Dakota that participated in the Strong
Heart Study
 Sampling method:
Stratified random sampling will be performed. The samples will be stratified based
on gender to ensure equal representation of both men and women. This is
important because cardiovascular disease (CVD), as well as the exposures of
interest (CVD risk factors, including type 2 diabetes and hypertension), is known to
occur at different rates among Native American men and women (3,4,5). Randomly
sampling for each sex separately will minimize any bias that this confounding
variable (i.e. gender) may cause and will allow for the association between CVD risk
factors and premature heart disease mortality to be determined separately for each
gender. The need to examine inter-stratum variability in the analysis stage of the
study will also be reduced.
 Sampling unit:
Native Americans
 Study unit:
Native Americans
 Sample size (adjusted for a 20% non-response rate):
665 total participants (166 cases and 499 controls)
 Sample size (further adjusted for a 20% loss to follow-up rate):
832 total participants (208 cases and 624 controls)

References
2), 4-11.
Metabolism, 97(10), 3766-3774.
1250-1256.

Care, 25(2), 279-283.
104(23), 2855-2864.

MOP Section 7: Study Organization and Participant
Recruitment / Retention

Organizational Chart
Responsibilities of the Investigators / Study Staff:
Miranda Freeman, Epidemiologist, PI
 Development and maintenance of the MOP
 Finalizing study protocol
 Preparing study materials
 Ensuring compliance to the protocol, MOP, IRB, federal and state regulations
 Participant recruitment, screening, and enrollment
 Ensuring the protection of participants’ rights
 Quality control
 Distribution of any changes to reports and documents to the funding agency
when needed
 Scientific direction of the study
 Fiscal direction of the study
 Delegating tasks to the other investigators
 Reports (e.g. enrollment, quality control, results)
Amanda Lynn, Study Coordinator
 Administrative support

 Communications (including the scheduling of meetings and training sessions, as
well as responding to and documenting ad hoc communications)
Jane Doe, Staff
 Aiding the study coordinator in her duties
John Deer, Staff
 Aiding the study coordinator in her duties
Dan D. Lyons, Data Extractor
 Data collection from the Strong Heart Study database
Yu Nguyen, Data Manager
 Data entry
 Data management
 Data transfer
 Quality control (i.e. error identification and correction)
Leigh King, Database Developer
 Developing a secure database to store collected data
 Data entry
Iona Ford, Database Manager
 Data management
 Quality control (i.e. error identification and correction)
Paige Turner, Biostatistician, Co-I
 Data analysis

 Calculating measures of association (e.g. odds ratios)
Annie Howe, Post-doc
 Aiding the biostatistician in her duties
Brighton Early, Post-doc
 Aiding the biostatistician in her duties
Chris P. Bacon, Cardiovascular Disease Expert, Consultant
 Counseling on cardiovascular disease when needed
Anita Knapp, Type 2 Diabetes Expert, Consultant
 Counseling on type 2 diabetes when needed

Agenda for Study Personnel Training
Day Time Task Trainers Trainees
7/10/14 9:00 – 9:30 am
Welcome /
Introductions
Miranda F.
Study
Personnel
9:30 – 10:30 am About the Study
Miranda F. &
Amanda L.
Study
Personnel
10:30 – 11:30am Consent / Enrollment Amanda L.
Study
Coordinator
Staff
11:30 – 12:30 pm Lunch / Questions
12:30 – 2:30 pm Data Extraction / Entry Dan D. L.
Database
Personnel
2:30 – 4:30 pm Data Management Yu N.
Database
Personnel
4:30 pm Adjourn

Recruitment Plan
Participants will be recruited from an ongoing prospective cohort (known as the
Strong Heart Study (SHS)) of Native Americans initiated in October 1988 by the
National Heart, Lung, and Blood Institute (NHLBI). Participants of the SHS reside on
various reservations in Arizona, Oklahoma, and North and South Dakota. Individuals
were recruited into the SHS cohort by local community members who traveled door to
door to locate eligible participants. Local community events, as well as advertisements
in local newspapers and radio stations, were also utilized in participant recruitment.
Further recruiting was done by Indian Health Service personnel and clinic staff (4). For
the purposes of this nested case-control study, individuals residing in North and South
Dakota that participated in the SHS will be selected through random sampling methods.
Individuals that belong in the control group will need to be contacted to gain consent to
use their information, while family members of the cases may need to give consent for
these individuals (since all the cases are deceased).

Incentive Plan
Since a case-control study design is being used, and all the data will be collected
retrospectively at one point in time, a retention plan is not applicable to this study.
Helping individuals understand that the results of the study could provide new
information that may help limit the number of premature deaths due to cardiovascular
disease in their community as well as other similar, Native American communities may
help them to agree to consent to the use of their information collected by the SHS.
Developing outreach programs and service projects in their communities could also help
incentivize their participation. Monetary awards may be too coercive since all that is
needed is their consent to use their SHS data.

References
2), 4-11.
Metabolism, 97(10), 3766-3774.
1250-1256.

Care, 25(2), 279-283.
104(23), 2855-2864.

Target Population
Person: Native American men and women; aged 45-64 years
Place: North and South Dakota
Time: January 1, 2010 to December 31, 2013
Study Design: Nested case-control study
Eligibility Criteria
Inclusion Criteria:
Cases:
 Mortality at < 65 years of age due to cardiovascular disease (CVD)
 Identification as Native American
 Resident member of a tribal community located in North and South Dakota
 Participation in the Strong Heart Study (SHS) between 2010 and 2013
 Surviving family members willing to consent to the use of the participant’s
information
Controls:
 Survival to ≥ 65 years of age
 Identification as Native American
 Resident member of a tribal community located in North and South Dakota
 Participation in the SHS between 2010 and 2013
 Willing to consent to the use of their information
Exclusion Criteria:
Cases:
 Mortality at < 45 years of age

Controls:
 Occurrence of > 2 CVD events (i.e. myocardial infarction or stroke) prior to the
age of 65
Accrual Log
Serial
#
Date
Screened
(mm/dd/yy)
Sex
# of
CVD
events
(<65yrs)
Mortality
from
CVD?
If
yes,
age
at
death
If no,
current
age
Eligible? If no,
reason for
ineligibility
Consent
obtained?
If no,
reason(s)
for not
participatingYes No Yes No Yes No
001 □ □ □ □ □ □
002 □ □ □ □ □ □
003 □ □ □ □ □ □
004 □ □ □ □ □ □
etc. □ □ □ □ □ □

MOP Section 8: Measurement

Participant ID: ___ ___ ___
Screener ID: ___ ___ ___
Date Screened: __ __ / __ __ / __ __ __ __
(mm / dd / yyyy )
PHDMNA_Form01_Page 1 of 2 M_Freeman_8.1_version2
Screening Instrument
1. Was the individual identified as being Native American?
□ Yes
□ No
2. Did the individual reside in North or South Dakota as part of a tribal community
between the years of 2010 and 2013?
□ Yes
□ No
3. Did the individual participate in the Strong Heart Study between the years of
2010 and 2013?
□ Yes
□ No
4. Is the individual currently living?
□ Yes (Continue to Question 5)
□ No (Skip to Question 6)
5. How old is the individual?
______ years
a. What is their birthdate?
__ __ / __ __ / __ __ __ __ (mm/dd/yyyy)

6. How old was the individual when they passed away?
_______ years
a. What is their date of death?
__ __ / __ __ / __ __ __ __ (mm/dd/yyyy)
7. If the answer to question 4 was “No,” was the individual reported to have died
from Cardiovascular Disease (CVD)?
□ Yes
□ No
8. How many Cardiovascular Disease events (i.e. myocardial infarction/heart attack
or stroke) is the individual reported as having prior to the age of 65 (not including
those who died of CVD at < 65 years of age)?
_____ CVD events
9. Did the individual, or their surviving family members, consent to the use of their
information?
□ Yes
□ No
10.Is the individual eligible to participate in the PHDMNA study?
□ Yes
□ No

PHDMNA_Procedure01_Page 1 of 3 M_Freeman_8.1_version2
Standard Operating Procedures
PREMATURE HEART DISEASE MORTALITY IN NATIVE AMERICANS (PHDMNA)
Instructions for the Use of:
PHDMNA_Form01
DATE OF CONSTRUCTION/REVISION: 3/25/2014
PURPOSE: To verify that all participants enrolled in the study meet the eligibility
criteria.
WHO USES IT: Study Coordinator (Amanda Lynn)
Study Coordinator Staff (Jane Doe and John Deer)
Data Extractor (Dan D. Lyons)
STAGE OF PROJECT FORM IS USED: Participant Screening
DEFINITION OF ITEMS AND INSTRUCTIONS FOR USE:
1. Enter the identification number of the individual being examined for eligibility in
the spaces provided next to “Participant ID” at the top of the form.
2. Enter the identification number of the person doing the screening in the spaces
provided next to “Screener ID” at the top of the form.
3. Enter the date that the screening is being performed in the spaces provided next
to “Date Screened” at the top of the page. Document the month first, followed by
day, then year. Use all of the spaces provided. If single digits are used for the
month or day spaces, fill blank spaces with a zero.
4. If any mistakes are made while filling out the screening instrument, then cross
out the wrong answer, initial next to the mistake, then write the correct response.

5. For Question 1, check the “Yes” box if the individual was identified as being
Native American. Check the “No” box if the individual was not identified as being
Native American.
6. For Question 2, check the “Yes” box if the individual resided in North or South
Dakota as part of a tribal community between the years of 2010 and 2013.
Check the “No” box if the individual did not reside in North or South Dakota
and/or was not part of a tribal community between Jan. 1, 2010 and Dec. 31,
2013.
7. For Question 3, check the “Yes” box if the individual participated in the Strong
Heart Study between the years of 2010 and 2013. Check the “No” box if the
individual did not participate in the Strong Heart Study and/or did not participate
during the time between Jan. 1, 2010 and Dec. 31, 2013.
8. For Question 4, check the “Yes” box if the individual is still alive at the time of
screening then continue to Question 5. Check the “No” box if the individual is
deceased and skip to Question 6.
9. For Question 5, if the individual is still alive at the time of screening then
document this by filling in the space below the question with their age (in years).
If the individual is deceased, then leave the question unanswered.
10.For Question 5a, document the date that the individual was born in the spaces
provided below the question. Document the month of birth first, followed by day,
then year of birth. Use all the spaces provided. If single digits are used for the
11.For Question 6, if the individual is deceased at the time of screening then
document how old they were when they died by filling in the space below the
question with their age (in years). If the individual is still living, then leave this
question unanswered.

12.For Question 6a, document the date that the individual died in the spaces
provided below the question. Document the month of death first, followed by
day, then year. Use all the spaces provided. If single digits are used for the
13.For Question 7, if the individual is deceased at the time of screening then
document if they were reported as having died from Cardiovascular Disease
(CVD). Check the “Yes” box if they were reported as having died from CVD.
Check the “No” box if they were not reported as having died from CVD. If the
individual is still living, then leave this question unanswered.
14.For Question 8, document the number of CVD events the individual was reported
as having prior to the age of 65 by filling in the space below the question with this
number. Only include incidences of stroke and heart attack in calculating this
value. Do not fill out this question for those that died of CVD at < 65 years of
age.
15.For Question 9, document whether or not the individual (if he/she is still living), or
their surviving family members (if the individual is deceased), consented to the
use of the individual’s information for this study. Check the “Yes” box if informed
consent was obtained. Check the “No” box if informed consent was not obtained.
16.For Question 10, based on the answers to the previous nine questions,
determine if the individual is eligible to participate in the study. Check the “Yes”
box if the individual is eligible. Check the “No” box if the individual is not eligible.

Table of Selected Measures for the Premature Heart Disease Mortality in Native
Americans (PHDMNA) Study
Measured
Construct
Form Number Selected Measure
Measurement Properties
Reliability Validity
Screening
Instrument
PHDMNA_01
N/A—Instrument created to track
eligibility criteria
Informed
Consent
PHDMNA_02
N/A—Form created for Human Subjects
Protection
Exposure Variables
Type 2 Diabetes
Cardiovascular
Disease Risk
Factors
PHDMNA_03
Oral Glucose-
Tolerance Test14
Test-retest
reliability:
Kappa=0.4315
Convergent validity
(Euglycemic Insulin
Clamp): r=0.739
Obesity
Bioelectrical
Impedance Meter5
Test-retest
reliability:
Correlation
Coefficient=0.966
Convergent validity
( Body Mass
Index): r=0.896
Hypertension
Blood Pressure
(Auscultatory
Sphygmomanometry)2
Not available
Convergent validity
(Oscillometric
readings at the
wrist): r=0.8617
LDL cholesterol
Β-quantitation
Procedure10
Not available
Convergent validity
( Direct
Immunoseparation
Method): r=0.9216
Albuminuria
Urine Albumin
Concentration7
Not available
Convergent validity
(Urinary Albumin
Excretion Rate):
r=0.811
Smoking Status
Self-Reported
Tobacco Use4
Not available
Convergent validity
(Cotinine):
Specificity=
89.2%11
Outcome Variables
Premature Heart
Disease
Mortality
PHDMNA_04 N/A N/A
Potential Confounding Variables
Sex
Participant
Characteristics
* PHDMNA_05 Data Abstraction Tool
N/A—Form created to abstract data from
the Strong Heart Study (SHS)
Socioeconomic
Status
Alcohol
Consumption

Diet
24-hour Food Recall
Survey13
Not available
Convergent validity
( Observed food
intake): r=0.668
Physical Activity
Modified Physical
Activity Questionnaire3
Test-retest
reliability:
Kappa= 0.40-
0.5112
Not available
Menopause
Status
Data Abstraction Tool
N/A—Form created to abstract data from
the SHS
Number of
Pregnancies
Hormone
Replacement
Therapy
Potential Effect Modifying Variables
Family History PHDMNA_06 N/A N/A
References
1. Ahn, C. W., Song, Y. D., Kim, J. H., Lim, S. K., Choi, K. H., Kim, K. R., ... & Huh,
K. B. (1999). The validity of random urine specimen albumin measurement as a
screening test for diabetic nephropathy. Yonsei Med J, 40(1), 40-5.
2. Beevers, G., Lip, G. Y., & O'Brien, E. (2001). ABC of hypertension: Blood
pressure measurement: Part II—Conventional sphygmomanometry: technique of
auscultatory blood pressure measurement. BMJ: British Medical
Journal,322(7293), 1043.
3. Evenson, K. R., & McGinn, A. P. (2005). Test-retest reliability of adult
surveillance measures for physical activity and inactivity. American journal of
preventive medicine, 28(5), 470-478.
4. Gorber, S. C., Schofield-Hurwitz, S., Hardt, J., Levasseur, G., & Tremblay, M.
(2009). The accuracy of self-reported smoking: a systematic review of the
relationship between self-reported and cotinine-assessed smoking status.
Nicotine & Tobacco Research, 11(1), 12-24.
5. Heber, D., Ingles, S., Ashley, J. M., Maxwell, M. H., Lyons, R. F., & Elashoff, R.
M. (1996). Clinical detection of sarcopenic obesity by bioelectrical impedance
analysis. The American journal of clinical nutrition, 64(3), 472S-477S.

6. Jackson, A. S., Pollock, M. L., Graves, J. E., & Mahar, M. T. (1988). Reliability
and validity of bioelectrical impedance in determining body composition. J Appl
Physiol, 64(2), 529-534.
7. Jafar, T. H., Chaturvedi, N., Hatcher, J., & Levey, A. S. (2007). Use of albumin
creatinine ratio and urine albumin concentration as a screening test for
albuminuria in an Indo-Asian population. Nephrology Dialysis
Transplantation,22(8), 2194-2200.
8. Karvetti, R. L., & Knuts, L. R. (1985). Validity of the 24-hour dietary recall.
Journal of the American Dietetic Association, 85(11), 1437-1442.
9. Matsuda, M., & DeFronzo, R. A. (1999). Insulin sensitivity indices obtained from
oral glucose tolerance testing: comparison with the euglycemic insulin clamp.
Diabetes care, 22(9), 1462-1470.
10.Nauck, M., Warnick, G. R., & Rifai, N. (2002). Methods for measurement of LDL-
cholesterol: a critical assessment of direct measurement by homogeneous
assays versus calculation. Clinical chemistry, 48(2), 236-254.
11.Patrick, D. L., Cheadle, A., Thompson, D. C., Diehr, P., Koepsell, T., & Kinne, S.
(1994). The validity of self-reported smoking: a review and meta-analysis.
American journal of public health, 84(7), 1086-1093.
12.Pierannunzi, C., Hu, S. S., & Balluz, L. (2013). A systematic review of
publications assessing reliability and validity of the Behavioral Risk Factor
Surveillance System (BRFSS), 2004–2011. BMC medical research
methodology, 13(1), 1-14.
13.Schatzkin, A., Kipnis, V., Carroll, R. J., Midthune, D., Subar, A. F., Bingham, S.,
... & Freedman, L. S. (2003). A comparison of a food frequency questionnaire
with a 24-hour recall for use in an epidemiological cohort study: results from the
biomarker-based Observing Protein and Energy Nutrition (OPEN)
study. International Journal of Epidemiology, 32(6), 1054-1062.
14.Stumvoll, M., Mitrakou, A., Pimenta, W., Jenssen, T. R. O. N. D., Yki-Järvinen, H.
A. N. N. E. L. E., Van Haeften, T., ... & Gerich, J. (2000). Use of the oral glucose
tolerance test to assess insulin release and insulin sensitivity. Diabetes
care, 23(3), 295-301.

15.Wallander, M., Malmberg, K., Norhammar, A., Rydén, L., & Tenerz, Å. (2008).
Oral Glucose Tolerance Test: A Reliable Tool for Early Detection of Glucose
Abnormalities in Patients With Acute Myocardial Infarction in Clinical Practice A
report on repeated oral glucose tolerance tests from the GAMI Study. Diabetes
Care, 31(1), 36-38.
16.Whiting, M. J., Shephard, M. D., & Tallis, G. A. (1997). Measurement of plasma
LDL cholesterol in patients with diabetes. Diabetes Care, 20(1), 12-14.
17.Zweiker, R., Schumacher, M., Fruhwald, F. M., Watzinger, N., & Klein, W. (2000).
Comparison of wrist blood pressure measurement with conventional
sphygmomanometry at a cardiology outpatient clinic. Journal of
hypertension,18(8), 1013-1018.

Participant ID: __ __ __
Data Collector: __ __ __
Date of Data Abstraction: __ __ /__ __ /__ __ __ __
(mm / dd / yyyy)
Participant Characteristics
Use the data abstracted from the Strong Heart Study (SHS) to answer the following
questions about the participant. Record any lack of documentation by checking the
“Can’t determine / Missing” box. Each question should only have one box selected.
Sex
1. What is the participant’s sex?
□ Male
□ Female
If the participant is female, continue to question 2. If the participant is male, code
questions 2 through 4 as “not applicable” and move on to question 5.
Female Physical History
2. Has she undergone menopause?
□ Yes
□ No
□ Not applicable
□ Can’t determine / Missing

3. How many times has she reported being pregnant?
□ Never
□ Once
□ Twice
□ Three times
□ More than three times
□ Not applicable
4. Has she ever participated in Hormone Replacement Therapy?
□ Yes
□ No
□ Not applicable

Socioeconomic Status
5. What is the participant’s yearly income?
□ < $25,000
□ $25,000 -- $40,000
□ $40,001 -- $60,000
□ $60,001 -- $80,000
□ $80,001 -- $100,000
□ > $100,000
6. What is the participant’s highest level of educational attainment?
□ Middle School
□ Some High School
□ GED or High School Graduate
□ Some College
□ College Graduate

7. Does the participant own their home?
□ Yes
□ No
Alcohol Consumption
8. How many times per week did the participant report drinking alcohol?
□ Never
□ Once per week
□ 2 – 3 times per week
□ More than 5 times per week
9. How many alcoholic drinks did the participant report having per day?
□ None
□ 1
□ 2 – 3
□ 4 or more

Diet
10.How many servings of fruits and vegetables did the participant report eating
every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
11.How many servings of grain did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5

12.How many servings of protein did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
13.How many servings of dairy did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
14.How many servings of fatty food did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5

15.How many servings of sugary food did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
16.How many times did the participant report eating-out each week?
□ 0
□ 1
□ 2 – 3
□ 4 or more
Physical Activity
17.How many times per week did the participant report getting moderate exercise?
□ 0
□ 1 – 2
□ 3 – 4
□ 5 or more

18.Did the participant report having difficulty walking or climbing stairs?
□ Yes
□ No
19.Did the participant report having an impairment that limits their physical activity?
□ Yes
□ No

PREMATURE HEART DISEASE MORTALITY IN NATIVE AMERICANS (PHDMNA)
Instructions for the Use of:
PHDMNA_Form05
DATE OF CONSTRUCTION/REVISION: 3/27/2014
PURPOSE: To gather information on the characteristics of a participant using the data
extracted from the Strong Heart Study.
WHO USES IT: Data Extractor (Dan D. Lyons)
Data Collectors
STAGE OF PROJECT FORM IS USED: Data Collection
DEFINITION OF ITEMS AND INSTRUCTIONS FOR USE:
1. Document the participant’s sex by either checking the box labeled “Male” or the
box labeled “Female.”
2. Record if the participant has undergone menopause by checking the correct box.
If the participant is male, select “not applicable.” If this information is missing
from the abstracted data, select “can’t determine/missing.”
3. Select the number of times the participant has been pregnant. If the participant
is male, select “not applicable.” If this information is missing from the abstracted
data, select “can’t determine/missing.”
4. Document if the participant has ever undergone Hormone Replacement Therapy.
If the participant is male, select “not applicable.” If this information is missing
from the abstracted data, select “can’t determine/missing.”
5. Document how much money the participant reported making in a year by
selecting the correct range. If this information is missing from the abstracted
6. Select the maximum amount of education the participant has received. If this
information is missing from the abstracted data, select “can’t determine/missing.”

7. Document if the participant owns the home he/she lives in (i.e. the house is in
their name). If this information is missing from the abstracted data, select “can’t
determine/missing.”
8. Report the number of times the participant reporting drinking alcohol every week
by selecting the correct box. If this information is missing from the abstracted
9. Select the correct number of drinks the participant reported drinking every day. If
this information is missing from the abstracted data, select “can’t
10.Document the number of servings of fruits and vegetables the participant
reported eating every day. If this information is missing from the abstracted data,
select “can’t determine/missing.”
11.Document the number of servings of grains the participant reported eating every
day. If this information is missing from the abstracted data, select “can’t
12.Document the number of servings of protein the participant reported eating every
13.Document the number of servings of dairy the participant reported eating every
14.Document the number of servings of fatty foods the participant reported eating
every day. If this information is missing from the abstracted data, select “can’t
15.Document the number of servings of sugary foods the participant reported eating
every day. If this information is missing from the abstracted data, select “can’t
16.Report the number of times the participant reported eating out each week (fast
food or restaurants). If this information is missing from the abstracted data,
select “can’t determine/missing.”

17.Select the number of times the participant would get moderate exercise each
week. If this information is missing from the abstracted data, select “can’t
18.Report if the participant had trouble walking or climbing up stairs by selecting the
correct box. If this information is missing from the abstracted data, select “can’t
19.Report if the participant’s physical activity was limited due to an impairment/
disability. If this information is missing from the abstracted data, select “can’t

MOP Section 9: Study Protocol and Communication

Study Protocol and Evaluation Schedule
Order of Data Collection Information Collected
1 Screening Instrument
2 Informed Consent
3 Participant Characteristics*
4 Reported Family History of Cardiovascular Disease
5 Record of Premature Heart Disease Mortality**
6 Oral Glucose-Tolerance Test Results
7 Bioelectrical Impedance Meter Results
8 Blood Pressure
9 B-quantitation Procedure Results
10 Urine Albumin Concentration
11 Smoking Status
*Participant Characteristics include: sex, weight, height, socioeconomic status, alcohol
consumption, diet (based on a 24-hour Food Recall Survey), physical activity measures
(based on the results of a modified Physical Activity Questionnaire), menopause status,
number of pregnancies, and use of Hormone Replacement Therapy.
**If applicable.

Participant ID: ________________
Participant Checklist
Form Form Collected
Date Entered
(mm/dd/yyyy)
Staff
Initials
PHDMNA_01 – Screening Instrument □ Yes / □ No / □ N/A
PHDMNA_02 – Informed Consent □ Yes / □ No / □ N/A
PHDMNA_03 – Cardiovascular
Disease Risk Factors
□ Yes / □ No / □ N/A
PHDMNA_04 – Report of Premature
Heart Disease Mortality
□ Yes / □ No / □ N/A
PHDMNA_05 – Participant
Characteristics
□ Yes / □ No / □ N/A
PHDMNA_06 – Family History of
Cardiovascular Disease
□ Yes / □ No / □ N/A

MOP Section 10: Data Entry / Management

Participant ID: __ __ __
Data Collector: __ __ __
Date of Data Abstraction: __ __ /__ __ /__ __ __ __
(mm / dd / yyyy)
PHD05PTID
PHD05DCID
PHD05Q1
PHD05Q2
Annotate Form:
Use the data abstracted from the Strong Heart Study (SHS) to answer the following
questions about the participant. Record any lack of documentation by checking the
“Can’t determine / Missing” box. Each question should only have one box selected.
Sex
1. What is the participant’s sex?
□ Male
□ Female
If the participant is female, continue to question 2. If the participant is male, code
questions 2 through 4 as “not applicable” and move on to question 5.
Female Physical History
2. Has she undergone menopause?
□ Yes
□ No
□ Not applicable
PHD05DATE
1
2
1
2
9
8

PHD05Q3
PHD05Q4
3. How many times has she reported being pregnant?
□ Never
□ Once
□ Twice
□ Three times
□ More than three times
□ Not applicable
4. Has she ever participated in Hormone Replacement Therapy?
□ Yes
□ No
□ Not applicable
1
2
8
9
1
2
3
4
5
8
9

PHD05Q5
PHD05Q6
5. What is the participant’s yearly income?
□ < $25,000
□ $25,000 -- $40,000
□ $40,001 -- $60,000
□ $60,001 -- $80,000
□ $80,001 -- $100,000
□ > $100,000
6. What is the participant’s highest level of educational attainment?
□ Middle School
□ Some High School
□ GED or High School Graduate
□ Some College
□ College Graduate
1
2
3
4
5
6
9
1
2
3
4
5
9

PHD05Q7
PHD05Q8
PHD05Q9
7. Does the participant own their home?
□ Yes
□ No
Alcohol Consumption
8. How many times per week did the participant report drinking alcohol?
□ Never
□ Once per week
□ More than 5 times per week
9. How many alcoholic drinks did the participant report having per day?
□ None
□ 1
□ 2 – 3
□ 4 or more
1
2
9
1
2
3
4
5
9
9
1
2
3
4

PHD05Q10
PHD05Q11
Diet
10.How many servings of fruits and vegetables did the participant report eating
every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
11.How many servings of grain did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
9
1
2
3
4
9
1
3
2
4

PHD05Q12
PHD05Q13
PHD05Q14
12.How many servings of protein did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
13.How many servings of dairy did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
14.How many servings of fatty food did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
9
4
3
2
1
9
4
3
2
1
1
2
3
4
9

PHD05Q16
PHD05Q17
PHD05Q15 15.How many servings of sugary food did the participant report eating every day?
□ 0
□ 1
□ 2 – 3
□ 4 – 5
16.How many times did the participant report eating-out each week?
□ 0
□ 1
□ 2 – 3
□ 4 or more
Physical Activity
17.How many times per week did the participant report getting moderate exercise?
□ 0
□ 1 – 2
□ 3 – 4
□ 5 or more
□ Can’t determine / Missing9
4
3
2
1
1
2
3
4
9
9
4
3
2
1

PHD05Q18
PHD05Q19
18.Did the participant report having difficulty walking or climbing stairs?
□ Yes
□ No
19.Did the participant report having an impairment that limits their physical activity?
□ Yes
□ No
9
2
1
1
2
9

Codebook
Potential Confounding Variables
Variable
Name
Type Description Response Options
PHD05PTID N, continuous
Participant identification
number
PHD05DCID N, continuous
Data Collector
identification number
PHD05DATE N, categorical
Date the form was
completed (mm/dd/yyyy)
PHD05Q1 N, categorical Participant’s sex
1 – Male
2 – Female
PHD05Q2 N, categorical
Participant’s menopause
status
1 – Yes
2 – No
8 – N/A
9 – Missing
Number of times the
participant has been
pregnant
1 – Never
2 – Once
3 – Twice
4 – Three times
5 – > 3 times
8 – N/A
9 – Missing
Participant’s use of
Hormone Replacement
Therapy
1 – Yes
2 – No
8 – N/A
9 – Missing
Participant’s yearly
income
1 – < $25,000
2 – $25,000-$40,000
3 – $40,001-$60,000

4 – $60,001-$80,000
5 – $80,001-$100,000
6 – > $100,000
9 – Missing
Participant’s education
level
1 – Middle school
2 – Some high school
3 – GED or High School
Graduate
4 – Some college
5 – College graduate
9 – Missing
Participant home
ownership
1 – Yes
2 – No
9 – Missing
Participant’s weekly
alcohol intake
1 – Never
2 – Once
3 – Two to three times
4 – Four to five times
5 – > 5 times
9 – Missing
Participant’s daily
alcoholic drink intake
1 – None
2 – One drink
3 – Two to three drinks
4 – ≥ Four drinks
9 – Missing
Participant’s daily fruit
and vegetable intake
1 – Zero servings
2 – One serving
3 – Two to three servings
4 – Four to five servings
9 – Missing
Participant’s daily grain
intake
Participant’s daily protein
intake

Participant’s daily dairy
intake
Participant’s daily intake
of fatty foods
Participant’s daily intake
of sugary foods
The number of times the
participant eats-out each
week
1 – Zero
2 – One time
3 – Two to three times
4 – ≥ Four times
9 – Missing
Participant’s weekly
moderate exercise
frequency
1 – Zero
2 – One to two times
3 – Three to four times
4 – ≥ Five times
9 – Missing
Participant’s reported
difficulty walking or
climbing stairs
1 – Yes
2 – No
9 – Missing
Participant’s possession
of a physical-activity-
limiting impairment
1 – Yes
2 – No
9 – Missing

In addition to analyzing the individual items listed above, variables were created
to examine various exposures of interest. For instance, a variable to classify
participants into different hypertension categories was created based on abstracted
blood pressure measures (e.g. systolic and diastolic blood pressure).
Calculated Variable (used for categorization)
Section 1.3: Exposure Variables
_HYP4CAT Calculated variable for three-categories of hypertension. _HYP4CAT is
derived from _SYSBP and _DIABP.
1
Normal/ Non-
hypertensive
Participants are classified as having normal blood pressure
based on their systolic and diastolic blood pressure.
(_SYSBP < 120 mmHg and _DIABP < 80 mmHg)
2 Prehypertension
Participants are classified as being pre-hypertensive based
on their systolic and diastolic blood pressure. (120 <
_SYSBP < 139 mmHg or 80 < _DIABP < 89 mmHg)
3 Hypertension
Participants are classified as being hypertensive based on
their systolic and diastolic blood pressure. (_SYSBP ≥ 140
mmHg or _DIABP ≥ 90 mmHg)
9
Can’t Determine/
Missing
Participants with an unknown or missing value for systolic or
diastolic blood pressure. (_SYSBP = 9 or _DIABP = 9)
SAS Code
IF (0.00 LE _SYSBP < 120) AND (0.00 LE _DIABP < 80)
THEN _HYP4CAT=1;
ELSE IF (120 LE _SYSBP < 139) OR (80 LE _DIABP <89)
THEN _HYP4CAT=2;
ELSE IF (140 LE _SYSBP < 300) OR (90 LE _DIABP <200)
THEN _HYP4CAT=3;
ELSE IF (_SYSBP = 9) OR ( _DIABP = 9) THEN
_HYP4CAT=9.

MOP Section 11: Data Analysis

Analysis Plan
Table 1: Descriptive Characteristics of the Premature Heart Disease Mortality in Native
Americans (PHDMNA) Study Participants.
Total
(n=832)
Cases
(n=208)
Controls
(n=624)
Sex N (%) N (%) N (%)
Male, %
Female, %
Income N (%) N (%) N (%)
Below the poverty line, %
Lower middle class, %
Upper middle class, %
Education N (%) N (%) N (%)
Less than High School, %
High School Graduate, %
College Graduate, %
Alcohol Consumption N (%) N (%) N (%)
Non-heavy drinkers, %
Heavy drinkers, %
Diet N (%) N (%) N (%)
Sub-optimal, %
Average, %
Optimal, %
Physical Activity (kcal/week) Median (IQR) Median (IQR) Median (IQR)
Menopause Status N (%) N (%) N (%)
Pre-menopausal, %
Post-menopausal, %

Number of Pregnancies N (%) N (%) N (%)
≤ 2 pregnancies, %
≥ 3 pregnancies, %
Hormone Replacement Therapy, % N (%) N (%) N (%)
Family History of CVD, % N (%) N (%) N (%)
CVD Risk Factors
Type 2 Diabetes, % N (%) N (%) N (%)
Obesity N (%) N (%) N (%)
Overweight, %
Obese, %
Hypertension, % N (%) N (%) N (%)
High LDL Cholesterol, % N (%) N (%) N (%)
Albuminuria N (%) N (%) N (%)
Microalbuminuria, %
Macroalbuminuria, %
Smoking Status N (%) N (%) N (%)
Non-smokers, %
Smokers, %
Premature Heart Disease Mortality, % N (%) N (%) N (%)
Footnote: CVD = Cardiovascular Disease; N (%) = frequency (percentage) of category
level; Median (IQR) = median (interquartile range)

Table 2: The unadjusted and adjusted association between type 2 diabetes and
premature heart disease mortality (n=832)
Unadjusted Model Adjusted Model
Type 2 Diabetes OR (95% CI) OR (95% CI)
Footnote: Model 1: type 2 diabetes; Model 2: type 2 diabetes plus sex, socioeconomic
status, alcohol consumption, diet, physical activity, menopause status, number of
pregnancies, hormone replacement therapy, and family history of cardiovascular
disease

MOP Section 12: Budget, Personnel Considerations,
and Timeline

PHDMNA Study Budget
Percent Calender Requested Fringe Funds
Base Salary Effort Months Salary Benefits Requested
A. Key Personnel
Miranda Freeman (PI) $100,000 25% 3.00 $25,000 $7,000.00 $32,000.00
Paige Turner (Co-I) $75,000 15% 1.80 $11,250 $3,150.00 $14,400.00
B. Other Personnel
Study Coordinator $65,000 65% 7.80 $42,250 $11,830.00 $54,080.00
Data Extractor $55,000 10% 1.20 $5,500 $1,540.00 $7,040.00
Data Manager $65,000 25% 3.00 $16,250 $4,550.00 $20,800.00
Database Developer $60,000 35% 4.20 $21,000 $5,880.00 $26,880.00
Database Manager $60,000 40% 4.80 $24,000 $6,720.00 $30,720.00
Post Doc $50,000 70% 8.40 $35,000 $9,800.00 $44,800.00
Post Doc $50,000 70% 8.40 $35,000 $9,800.00 $44,800.00
Staff $30,000 45% 5.40 $13,500 $3,780.00 $17,280.00
Staff $30,000 45% 5.40 $13,500 $3,780.00 $17,280.00
C. Equipment
D. Travel $3,000.00
E. Participant and Trainee Costs
F. Other Direct Costs
Materials and Supplies $5,000.00
Publication Costs $2,000.00
Type 2 Diabetes Consultant $2,000.00
Cardiovascular Disease Consultant $2,000.00
Total Personnel Direct Costs $310,080.00
Total Nonpersonnel Direct Costs $14,000.00
G. Total Direct Costs $324,080.00
H. Total Indirect Costs $168,521.60
I. Total Direct and Indirect Costs $492,601.60

Budget Justification
A. Key Personnel
Dr. Miranda Freeman, Primary Investigator (3 calendar months or 25%
effort) is a Professor at the Austin Regional Campus of The University of Texas Health
Science Center at Houston’s School of Public Health, in the Division of Epidemiology,
Human Genetics, and Environmental Sciences. She is a specialist in chronic disease
epidemiology with much experience in working with disadvantaged populations. She
has participated in several studies that involved people of diverse cultures and socio-
economic status, including Native Americans of the Pima tribe in Arizona. With her
experience in working with Native Americans, Dr. Freeman will lead the study. Along
with developing a manual of operating procedures, she will delegate tasks, oversee the
study’s completion, and aide in disseminating the results.
Dr. Paige Turner, Co-Investigator (1.8 calendar months or 15% effort) is a
Professor at the Austin Regional Campus of The University of Texas Health Science
Center at Houston’s School of Public Health, in the Division of Biostatistics. She is a
skilled biostatistician with a lot of experience in working with large datasets. Dr. Turner
is very experienced at working with data from populations with many potential
confounders, effect modifiers, and covariates that need to be controlled for, much like
this study has. Given her familiarity with such data and her experience as a Principle
Investigator or Co-Investigator on numerous other research projects, she will play a lead
role in the development of the study and will co-lead the research with the PI, Dr.
Miranda Freeman. Dr. Turner will participate in data analysis as well as administrative
support, and will co-author scientific papers based on the study findings.
B. Other Personnel
Amanda Lynn, Study Coordinator (7.8 calendar months or 65% effort) has
experience coordinating three large studies, two of which involved disadvantaged
populations of low socio-economic status. She also has experience with nested case-
control studies making her very capable of serving as the study coordinator for this
research project. She will be in charge of providing administrative support, screening

and enrolling participants into the study, as well as communication with those both in
and outside of the study. Two of her staff members, Jane Doe, Staff (5.4 calendar
months or 45% effort) and John Deer, Staff (5.4 calendar months or 45% effort),
have worked with her on two other research projects and are experienced in participant
screening and recruitment. They will serve as staff members for this study and will help
screen and enroll participants along with aiding Ms. Lynn when needed.
Dan D. Lyons, Data Extractor (1.2 calendar months or 10% effort) will be in
charge of extracting the necessary data from the Strong Heart Study database. He is a
senior employee of the Austin Regional Campus of The University of Texas Health
Science Center at Houston’s School of Public Health with experience extracting data for
dozens of retrospective studies making him very capable of the task. Yu Nguyen, Data
Manager (3 calendar months or 25% effort) is also a long time employee of the
Austin Regional Campus of The University of Texas Health Science Center at
Houston’s School of Public Health. He has two decades worth of experience entering
and managing data and will be in charge of overseeing the data entry, management,
transfer, and quality control for the study. Leigh King, Database Developer (4.2
calendar months or 35% effort) and Iona Ford, Database Manager (4.8 calendar
months or 40% effort) have a combined experience of working on over a dozen
research projects and are very familiar with the latest developments in database
security and management. Ms. King will be responsible for developing a secure
database to store the extracted data on as well as properly entering the data. Ms. Ford
will perform most of the data management and quality control tasks. Both of them will
be overseen by Mr. Nguyen.
Annie Howe, Post Doc (8.4 calendar months or 70% effort) and Brighton
Early, Post Doc (8.4 calendar months or 70% effort) have both recently graduated
from the Austin Regional Campus of The University of Texas Health Science Center at
Houston’s School of Public Health with doctorates in Biostatistics. Both graduated with
honors and have at least two years of experience in data analysis. They will be
responsible for aiding Dr. Turner in performing the necessary data analyses.

D. Travel
Funds are requested for the PI and Co-I to each attend a conference. The cost will
include airfare, hotel costs, conference registration fees, and per diem.
PI travel to one conference ($1,500 per conference) 1,500.00
Co-I travel to one conference ($1,500 per conference) 1,500.00
Total for PI and Co-I ($1,500 x 2 conferences) $3,000.00

Timeline
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Obtain IRB approval X
Acquire dataset from the
SHS Cohort
X X
Develop questionnaires/
pilot measures
X X X
Develop database X X X
Train staff X X
Pilot data extraction tools X X
Data extraction/entry X X X
Conduct analyses X X X X X X X
Communicate findings X X X X X

MOP Section 13: Final Considerations

M_Freeman_FINALMOP

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