[]{#__RefHeading___Toc502117145 .anchor}

**Risk Factors for Osteoporosis in Alaska Native Women:**

**A Cross Sectional Survey**

A Thesis

By

Joshua J. Filner

Presented to the Department of Public Health and Preventive Medicine

Oregon Health Sciences University

in Partial Fulfillment of the Requirements for the Degree of

Master of Public Health

April 2001

School of Medicine

Oregon Health Sciences University

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Certificate of Approval

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

This is to certify that the M.P.H. thesis of

Joshua J. Filner

has been approved

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

> Thomas Becker, M.D., Ph.D.
>
> Committee Chair

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

> Kelly Krohn, M.D.

Committee Member

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

> Jodi Lapidus, Ph.D.
>
> Committee Member

Table of Contents

List of Tables [iv](#list-of-tables)

List of Figures [v](#list-of-figures)

Acknowledgements [vii](#acknowledgements)

Funding provided by: [viii](#funding-provided-by)

Abstract [ix](#abstract)

Chapter 1 -- Introduction [1](#chapter-1-introduction)

Osteoporosis [1](#osteoporosis)

Motivation [4](#motivation)

Chapter 2 -- Methods [7](#chapter-2-methods)

Subject Recruitment [7](#subject-recruitment)

Data Collection Methods [8](#data-collection-methods)

Data Security and Quality Control
[10](#data-security-and-quality-control)

Definitions [10](#definitions)

Statistical Methods [11](#statistical-methods)

Power Analysis [11](#power-analysis)

Univariate Analysis [12](#univariate-analysis)

Regression Modeling [12](#regression-modeling)

Chapter 3 -- Results [14](#chapter-3-results)

Chapter 4 -- Discussion [25](#chapter-4-discussion)

Issues with Ultrasound Bone Density Technology
[27](#issues-with-ultrasound-bone-density-technology)

Limitations [28](#limitations)

Future Studies [30](#future-studies)

Conclusions [31](#conclusions)

References [32](#references)

Appendix A -- Consent Form [35](#appendix-a-consent-form)

Appendix B -- The Study Questionnaire
[37](#appendix-b-the-study-questionnaire)

Appendix C -- Methods Notes [39](#appendix-c-methods-notes)

Appendix D -- Ultrasound Methods 43

Appendix E -- Subject T-Score Report [Error! Bookmark not
defined.](#__RefHeading___Toc502117172)

#  

# List of Tables

Table 1-1 Selected Risk Factors for Osteoporosis According to the
National Osteoporosis Foundation [4](#__RefHeading___Toc502850833)

Table 2-1 Estimated Sample Size Given Different Assumptions of
Prevalence and Bone Density Cut Point for Alaska Natives vs. Non-Natives
[12](#__RefHeading___Toc502850834)

Table 2-2 Candidate Predictor Variables and Variable Type, Tested for
Use as Covariates in the Statistical Multivariate Analysis
[13](#__RefHeading___Toc502850835)

Table 3-1 Distribution of Study Subjects by Study Site
[18](#__RefHeading___Toc502850837)

Table 3-2 Selected Characteristics of Study Participants
[18](#__RefHeading___Toc502850836)

Table 3-3 Dietary Calcium Measures (mg/day) for All Subjects and for
Alaska Natives [19](#__RefHeading___Toc502850839)

Table 3-4 Bone Density Characteristics and Pack Years of Smoking for
Study Subjects [19](#__RefHeading___Toc502850838)

Table 3-5 Univariate Analysis of Subject Characteristics Tabulated by
Abnormal vs. Normal Bone Density [20](#__RefHeading___Toc502850840)

Table 3-6 Gamma Coefficients Among Selected Study Variables as an
Evaluation of Relatedness of Candidate Predictor Variables in a
Multivariate Analysis for Alaska Native Subjects Only
[21](#__RefHeading___Toc502117145)

Table 3-7 Gamma Coefficients Among Selected Study Variables as an
Evaluation of Relatedness of Candidate Predictor Variables in a
Multivariate Analysis for All Study Subjects
[22](#__RefHeading___Toc502850842)

Table 3-8 Predictors of Low Bone Density from a Multiple Logistic
Regression Model for Alaska Native Subjects Only
[23](#__RefHeading___Toc502850843)

Table 3-9 Predictors of Low Bone Density from a Multiple Logistic
Regression Model for Non Native and Alaska Native Study Subjects
[23](#__RefHeading___Toc502850844)

Table 3-10 Low Bone Density by Age Group for Alaska Natives
[24](#__RefHeading___Toc502850845)

Table A-1 Site Specific Recruitment Notes
[39](#__RefHeading___Toc502850846)

Table A-2 Elemental Calcium Content of Food Items per Serving Used in
Calculations of Dietary Calcium Intake
[40](#__RefHeading___Toc502850847)

# List of Figures

Figure 1-1 Normal Bone Density from Age 30 Years and Older 2

Figure 1-2 W.H.O. Classification of T-scores 3

Figure 1-3 Geographical Distribution of Alaska Natives by Tribal
Grouping 6

Figure 1-4 Alaska Population: Age and Sex Distribution by Race 6

Figure 2-1 Locations of Study Sites in Alaska 7

Figure A-1 Predicted Values vs. Cook's Distance from the Final Logistic
Regression Model, for All Natives 41

Figure A-2 Predicted Values vs. Pearson Residuals from the Final
Logistic Regression Model, for All Natives 41

Figure A-3 Predicted Values vs. Cook's Distance from a Logistic
Regression Model for all Variables Shown in Table 3-9 and an Age-Smoking
Interaction Term, for All Natives 42

Figure A-4 Predicted Values vs. Pearson Residuals from a Logistic
Regression Model for all Variables in Shown in Table 3-9 and an
Age-Smoking Interaction Term, for All Natives 42

Figure A-5 Ultrasound Quality Control Log for August 1999 44

Figure A-6 Ultrasound Quality Control Log for September 1999 45

# Précis

Mitch Greenlick likes to say that a thesis should be a journey. This
thesis certainly has been a journey, and a circuitous one at that. My
first exposure to osteoporosis was about six years ago; I got a call
while still working at the Reed College nuclear reactor. I spent several
months stuffing frozen dead mice into the reactor to measure calcium
levels. That experience ended my promising career as an animal
researcher.

A few years later, Kelly Krohn sent an e mail to all medical students
looking for someone to go to Alaska and study osteoporosis. I was still
interested in osteoporosis and this project didn't involve dead mice, so
off I went. Like any small student project, it was unfunded at the
start. Fortunately, Dr. Krohn and I were able to find enough funds to
make the project a reality.

The original plan was that this would be a small summer research
project. I was to learn about field epidemiology, travel, and maybe get
published in a scientific journal. I got to visit some incredibly remote
and beautiful places and meet a lot of wonderful people. At the end of
the summer I had some viable data.

You can always collect more data. There was also a great deal of
interest by both the health care providers and the patients whom I saw.
The project kept growing and I returned to Alaska. The final result is
this document. It is my sincere hope that in the end it will prove
useful in guiding the development of osteoporosis prevention programs
for Alaska Natives.

# Acknowledgements

The solo research project in epidemiology is nearly extinct. This
project is no exception and I am extremely grateful to the many people
who helped me. I am unable to list everyone by name. I apologize to
anyone I have omitted.

The nurses, health aides and clerical staff at all of the clinics
deserve special recognition for their help in recruiting, finding space
for me and making me welcome at the clinics. I am most indebted to the
people in the communities I visited, for their hospitality and for
participating in my study.

*My Thesis Committee:*

Thomas Becker, M.D., Ph.D., Kelly Krohn, M.D., Jodi Lapidus, Ph.D.

*Logistics, Advice, and Mentorship:*

Jane Kelly, M.D.

*Food, Lodging, Hospitality, Alaska Lore, and General Support*:

Donald, Kara, Kelly, and Lana Johnson, Adrienne Shipley

######## Proof reading and Editing:

Stephen G. Frantz, P.E., C.H.P.

Christopher Hoffman, M.S.

*Encouragement, Proof reading, and General Tolerance:*

Kristina Marie Rheaume

*The Indian Health Service's Alaska Diabetes Program Office Staff:*

Tammy Brown, R.D., Joan Hastie, Anne-Marie Mayer, F.N.P., M.P.H., Anita
Vogt, R.N.

#####  

*Advice:*

> Kenneth Faulkner, Ph.D., Michael Garland, D.Sci.Rel., Cynthia Morris,
> Ph.D., M.P.H., Gary Sexton, Ph.D.

######## OHSU Bone and Mineral Research Unit:

Eric Orwoll, M.D., Shelia Orwoll

####### Barrow:

Sharon Conn, R.N., Robert Haight, M.D.

####### Kotzabue:

Connie Rakoski, Jeanne Reichert, R.N., Janice Shakels, M.D., Ann Swisher

*St. Paul:*

Clinic Staff

*Alaska Native Medical Center*:

Department of Family Medicine -- Peter Mjos, M.D., Steven Vilter, M.D.,
Dr. Maria Freeman, M.D.

Department of Internal Medicine -- David Templin, M.D., David
Barrett**,** M.D.

Department of Orthopedics -- David Beck, M.D., Candice Clawson, M.D.,
William Pratt, M.D., R.J. Hall, M.D., Debbie Freudenthal, R.N

Department of Women's Health - Neil Murphy, M.D., Stephanie Ecklund,
M.D., Colleen Murphy, M.D., Pat Taylor, N.P.

####### 

####### Yukon Kuskaquim Health Corporation:

Roger Johnson, M.D.

*Kodiak Area Native Association*:

Dr. Tom Selinger, M.D., Dr. Dave Udder, LT. Felix Santiago, R.N.,
B.S.N., U.S.P.H.S.

####### 

####### Sand Point Medical:

Kathy, Marcy, Margaret, Terry, Ingrid

*King Cove:*

Jonathan, Leslie, Elisabeth Smaha

####### 

####### North Star Health Clinic (Seward):

Annette Siemans, F.N.P., Ellen O'Brian, F.N.P.

####### 

####### Port Graham Clinic:

Eleanor, Darlene

####### 

####### Tatitlek Clinic:

Laurinda Vlsaoff

####### 

####### Chenga Bay Clinic:

Cheryl Eleshansky

####### 

####### Nanwalek Clinic Staff: 

Effram

## Funding provided by:

The OHSU Summer Medical Student Research Program (N.I.H.) (1998 and
1999)

The N.L. Tartar Trust (1999)

The Portland V.A. Research Foundation (1998)

Chugachmuit Native Corporation (1998, 1999)

North Slope Native Corporation (1999)

Maniilaq Native Corporation (1999)

Alaska Area Diabetes Program (1998, 1999)

Aleutian Pribilof Island Association (1999)

Eastern Aleutian Tribes (1998)

*The use of the Ultrasound Device in 1999:*

Hologic, Inc., Waltham, MA

# Abstract

**Objective:** To estimate the prevalence of risk factors for
osteoporosis in Alaska Natives.

**Methods**: This study is a cross-sectional convenience sample of
patients from 17 clinics in Alaska. I interviewed subjects regarding
dietary calcium, demographics, and risk factors for osteoporosis. I
measured subjects' calcaneal bone density using quantitative ultrasound.

**Results:** I collected data from 452 women, 316 of whom were Alaska
Natives. Risk factors for osteoporosis were highly prevalent. Many
Alaska Natives were current smokers (45%), were former smokers (32%),
and 45% had low bone density (t-score \< -1.0). Total dietary calcium
intake was lower than recommended (median = 379 mg/day). Both current
(Odds Ratio (OR)=3.9, 95% Confidence Interval (95% C.I.)=1.8-8.4) and
former smokers (OR=2.8, 95% C.I.=1.3-6.2) were significantly more likely
to have low bone density than subjects who have never smoked. Chronic
users of oral steroids were also significantly more likely (OR=4.7, 95%
C.I.=1.8-12.0) to have low bone density than non-users of oral steroids.

**Conclusions**: Risk factors for low bone density and osteoporosis are
prevalent in Alaska Native women. The number of Alaska Native women at
risk for osteoporosis will increase during the next decade due to the
aging of this population. A comprehensive prevention program to reduce
the prevalence of modifiable risk factors in this population is
warranted.

# Chapter 1 -- Introduction

I examined the relationship between risk factors for low bone density
and bone density measurements in Alaska Native women. I implemented a
cross-sectional survey to evaluate a convenience sample of Alaska Native
and non-native women age 20 years and over.

## Osteoporosis

Osteoporosis is a disease in which the normal architecture of bone is
disrupted and the matrix of bone is demineralized. In a healthy adult,
two cell types with opposite functions are active in bone remodeling.
Osteoblasts synthesize the organic components of the bone matrix; over
time this matrix mineralizes. Osteoclasts, by contrast, break down or
resorb bone (Junqueira, 1995). When this process is under normal
homeostatic control, the amount of bone resorbed is roughly equal to the
amount of new matrix formed. In osteoporosis, this balance is disturbed
and osteoclasts resorb bone faster than osteoblasts can replace it. This
disruption of remodeling eventually leads to lower bone mass and a
change in bone architecture. These abnormalities reduce bone strength.
As a result, people with osteoporosis are at increased risk for
fractures. Most medical therapies for osteoporosis attempt to restore
the balance between osteoclasts and osteoblasts by inhibiting the
osteoclasts. More detailed discussions of the basic science of bone
metabolism can be found in other sources (Favus, 1996; Riggs, 1995).

Osteoporosis is divided into two categories, primary and secondary
(Rosenberg, 1994). Primary osteoporosis is associated with aging and is
more prevalent with menopause. Secondary osteoporosis is caused by a
factor other than age or menopause. Causes of secondary osteoporosis
include Cushing's syndrome, type 1 diabetes, alcohol, lithium,
anticonvulsants, malabsorption, and multiple myeloma.

Bone density changes with age in a characteristic pattern (Figure 1-1).
Bone progressively increases in density from a state of minimal
ossification in a newborn infant to peak density at approximately thirty
years of age. After age thirty, bone mass begins a slow and steady
decline until menopause. After menopause, bone mass declines rapidly
(Hansen, 1995). This postmenopausal bone loss is the reason why
osteoporosis prevention efforts are often focused on women at or just
past menopause.

Osteoporosis is technically a histologic diagnosis made using a biopsy
sample. A biopsy can detect both a decrease in ossification and
interruptions in the normal cross-linked structural network of bone
(Recker, 1996). In practice, biopsy is very rarely used to make a
diagnosis; various bone density measurement techniques are used instead.
Bone density measurements are non-invasive and are almost as accurate in
detecting osteoporosis.

![](media/image1.wmf) Bone density measurements are interpreted in three
ways, as g/cm^2^, as a t-score and as a z-score. A t-score is the number
of standard deviations from the mean density of young, normal,
sex-matched individuals. A z-score is the number of standard deviations
from the mean density for sex and age-matched individuals. The World
Health Organization (W.H.O.) developed the most widely accepted criteria
for classifying t-scores based on measurements taken at the femoral neck
or lumbar spine (Kanis, 1994). Under W.H.O. criteria (Figure 1-2), a
t-score greater than ‑1.0 is normal. A t-score between ‑1.0 and ‑2.5 is
classified as osteopenia, or low bone density. A score of less than ‑2.5
is classified as osteoporosis. These criteria apply only to measurements
taken at the femoral neck or lumbar spine.

The major health consequence of osteoporosis is increased risk of
fracture. A consensus panel study estimated that 90% of hip and spine
fractures in elderly white women are attributable to osteoporosis
(Melton, 1997). Hip and vertebral compression fractures are the most
important morbidities associated with osteoporosis, but many other types
of fractures are associated with this disease. A national study using
Medicare data found 5.3 discharges per 10,000 person-years with a
diagnosis of vertebral fracture for 65 year olds, and 47.8 discharges
per 10,000 person-years with a diagnosis of vertebral fracture for 90
year olds (Jacobsen, 1992). Hip and vertebral fractures have enormous
health and financial costs. One study estimated the annual U.S.
expenditures to treat osteoporotic fractures to be \$13.8 billion in
1995 (Ray, 1997). Hip fractures are associated with significant
mortality. One study found 8% mortality at 3 months post fracture (Cree,
2000). A nested case control study found 20% mortality at one year for
hip fracture patients at least 70 years old, with a crude relative risk
of 2.4 compared to age matched controls (Wolinsky, 1997). Hip fracture
patients were also hospitalized or institutionalized more frequently
after their fracture than age-matched controls. In addition to
traditional outcome measures, all of these fractures can affect quality
of life, activities of daily living, and body image.

Age-associated bone degeneration does not affect all women equally. The
list of known and putative risk factors for osteoporosis is quite long.
Risk factors identified by the National Osteoporosis Foundation are
shown in Table 1-1 (National Osteoporosis Foundation, 1999). Other
putative risk factors for osteoporosis are under investigation (Melton,
1996). More risk factors are likely to be identified in the future.

[]{#__RefHeading___Toc502850833 .anchor}Table 1-1 Selected Risk Factors
for Osteoporosis According to the National Osteoporosis Foundation

+-----------------------------+----------------------------------------+
| ##### N                     | **Potentially Modifiable Risk          |
| on-modifiable Risk Factors  | Factors**                              |
+-----------------------------+----------------------------------------+
| History of Fracture as an   | Current Cigarette Smoking              |
| Adult                       |                                        |
+-----------------------------+----------------------------------------+
| History of Fracture as an   | Low Body Weight (\<127 lb.)            |
| Adult in a First Degree     |                                        |
| Relative                    |                                        |
+-----------------------------+----------------------------------------+
| Caucasian Race              | Estrogen Deficiency:                   |
|                             |                                        |
|                             | -Early Menopause or Bilateral          |
|                             | Oophorectomy                           |
|                             |                                        |
|                             | -Premenopausal Amenorrhea (\>1 year)   |
+-----------------------------+----------------------------------------+
| Female Sex                  | Lifelong Low Calcium Intake            |
+-----------------------------+----------------------------------------+
| Dementia                    | Alcoholism                             |
+-----------------------------+----------------------------------------+
| Poor Health/Fragility       | Recurrent Falls                        |
+-----------------------------+----------------------------------------+
|                             | Inadequate Physical Activity           |
+-----------------------------+----------------------------------------+
|                             | Impaired Eyesight Despite Adequate     |
|                             | Correction                             |
+-----------------------------+----------------------------------------+

## Motivation 

The term "Alaska Natives" includes three culturally, linguistically and
genetically distinct groups: Eskimos, Indians, and Aleuts. These groups
traditionally inhabited different regions of Alaska (Figure 1-3). Within
each of these broad ethnic classifications exists substantial linguistic
and cultural heterogeneity. The diversity of indigenous Alaskan people
limits the value in attempting to make broad generalizations. Yet
studying each subgroup also presents challenges because of the large
number of subgroups and small population in some of the subgroups.

Health care resources for Alaska Natives have traditionally focused on
treating acute medical conditions. Infectious diseases receive a high
level of attention in Alaska Natives. This is appropriate because of
their high incidence. Botulism, for example, has an incidence of 10.7
per 100,000 person-years in Alaska, substantially higher than in any
other state (Beller, 1998).

Chronic diseases are beginning to receive more attention because of the
realization of their increasing impact on Alaskan Natives. More Alaska
Natives are living to the older ages associated with many chronic
diseases. Osteoporosis is one such chronic disease that is likely to be
increasing among Alaskan Natives, but few reports have been published on
the subject. A search of MEDLINE using Alaska Natives and osteoporosis
or bone density as keywords of records from 1965 through 1999 yields
less than 20 articles. Specialized references also yield a small number
of articles (Fortuine, 1993).

Some evidence suggests that Alaska Natives may have lower bone density
than Caucasians. A study published by the International Biological
Perspective found that Eskimo people had lower bone density than
Caucasians and that the difference between Eskimos and Caucasians was
more pronounced in the elderly (Mazess, 1978). A small study by the same
investigator found that Aleutian Islanders also have lower bone density
than Caucasians (Mazess, 1985).

# A large proportion of Alaska Natives in the baby boom generation is nearing menopause. Figure 1-4 shows the age structure of the population of the state of Alaska by race. Since osteoporosis is primarily a disease of post-menopausal women, osteoporosis will become an important issue in Alaska Native health care with the increase in life expectancy of Alaska Natives. Information about the distribution of risk factors in this population will be of use for planning prevention and intervention programs. My goal for this project was to provide an estimate of the prevalence of low bone density and the risk factors for low bone density in Alaska Natives.

# Chapter 2 -- Methods

This study is a cross sectional prevalence survey of Alaska Natives. I
selected a convenience sample of patients at 12 sites in Alaska and
collected data in Alaska from July 7 to Aug. 31, 1998 and Aug. 4 to
Sept. 21, 1999.

## Subject Recruitment

![](media/image2.wmf)

I included only females who were at least 20 years old. Potential
subjects were excluded if they failed to complete the study protocol or
if no valid density measurement could be taken. Clinic staff conducted
the initial recruitment of subjects, either face to face or by
telephone. At one village, the local radio station broadcast a public
service announcement to recruit subjects. All recruiters explained that
participation in the study was entirely voluntary and there was no cost
to the subject. No record was kept of the number of potential subjects
who declined to participate at initial recruitment. When subjects
presented for the study interview, I again explained the voluntary
nature of the study and offered to answer questions about the study. I
presented subjects still willing to participate in the study with a
consent form (Appendix A). For those who refused to participate at the
interview stage, I recorded the reason for refusal.

## Data Collection Methods 

After obtaining consent, I interviewed all of the subjects using a
closed response questionnaire (Appendix B) at 15 of the clinics. At two
clinics (Port Graham and Nanwalek) the subjects completed the
questionnaire themselves.

During the interview, I entered the subject responses directly into
database software (Epi Info 6.04b, CDC, Atlanta, GA) using a portable
computer. Due to time constraints, subjects in Nanwalek and in Port
Graham filled out paper versions of the questionnaire while waiting to
see me. In these cases, I verified the answers for a sample of questions
during the interview. These paper questionnaires were entered into the
database at a later time. I obtained questionnaire data before the bone
density measurement was taken.

I used the questionnaire data to estimate dietary calcium. During the
interview, I asked each subject how many servings per week in the last
year on average they ate a given food item. Estimating a serving size
for each food I determined the amount of calcium per serving from tables
of nutrient values (Appendix C). I used these values to calculate
calcium intake. The calcium measures are described in the definitions
section of this chapter.

I measured calcaneal bone density using a Sahara Clinical Bone Sonometer
(Hologic, Inc., Waltham, MA). I followed the recommendations of the
manufacturer for the use of this device (Appendix D). The right
calcaneus was measured unless the subject had a history of any injury to
the right foot or ankle (except sprains, bunions, or fracture of the
metatarsals or phalanges). If the first measurement was not
satisfactory, I repeated the measurement up to four times. If none of
these repeated measurements passed an automated reliability test, the
mean of the multiple measurements was used as that subject's bone
density for the statistical analysis. In some cases no valid measurement
was obtained from the subject; these subjects were excluded from the
analysis (n = 6). At the end of the study visit, I provided the subjects
with reports of their density results (Appendix E) and encouraged them
to discuss the results with their health-care provider.

I completed a standard Indian Health Service Form 803 ambulatory
encounter record for every study visit. This record included the
subject's t-score, estimated bone density, broadband ultrasound
attenuation, speed of sound through bone, and appropriate notations to
aid the interpretation of the results. Records completed in 1999 were
coded as a telephone visit to prevent inadvertent billing for
participation in the study by the hospital. My supervising physician at
each clinic read and signed the encounter records. I placed the
completed encounter record in the subject's medical chart along with a
photocopy of the consent form.

### Data Security and Quality Control

I verified the questionnaire responses by comparing selected response
variables with the data in individual electronic medical records.
Records for most subjects were available via direct dial-up access to
the computer system at the Alaska Native Medical Center. I determined
from the medication record which subjects were taking the following
drugs: alendronate, etidronate, estrogen, estrogen/progesterone
mixtures, calcium supplements, corticosteroids, calcitonin, raloxefene,
tamoxifen, phenobarbital, phenytoin and carbamazepine. Birth date and
diabetes diagnosis were also confirmed by electronic medical record.

I encrypted data files containing identifying information during the
course of this study when not in use. Identifying information such as
name and medical record number were stripped from the electronic data
files at the end of the data analysis to prevent any accidental
disclosure of confidential information.

### Definitions

The following definitions were used for the purposes of this study:

*Alaska Native*: I considered any person who self-identified as an
Alaska Native or American Indian. This included American Indians living
in Alaska but originally from other states.

*Clinic staff*: I defined clinic staff as any person who worked at the
clinic or hospital, including cleaning and kitchen staff.

*Salpingo-oophorectomy*: I considered a subject to have had a
salpingo-oophorectomy if she stated 1) that both of her ovaries were
removed, 2) that one ovary was removed, or 3) hysterectomy had been
performed but the subject was uncertain if her ovaries were still
intact.

*Calcium Supplement*: A calcium supplement was any dietary supplement
containing calcium. This included multivitamins containing calcium.

*Diabetic*: I considered any person who identified herself as a diabetic
to be a diabetic. Gestational diabetes mellitus and insulin resistance
were not considered diabetes for this study. Subject diabetes status was
confirmed as part of the review of the electronic medical record.

*Menopause:* I considered any woman who stated that she had started or
completed menopause to have started menopause. I also considered any
woman who stated she had surgical menopause to have started menopause.

*Total Daily Dietary Calcium*: I multiplied the number of servings per
week of each food by the amount of calcium per serving of that food. I
summed the values from all foods and divided by seven.

*Total Daily Dairy Calcium*: I multiplied the number of servings per
week of each dairy food by the amount of calcium per serving of that
food. I summed the values from all foods and divided by seven.

*Total Daily Calcium from Supplements*: I multiplied the number of days
per week supplements were used by the dose of calcium and divided by
seven.

*Total Daily Calcium Intake*: The sum of total dietary calcium and daily
calcium from supplements. This was the primary calcium measure used in
the statistical analysis.

## Statistical Methods

### Power Analysis

I performed an estimated power analysis using an estimated prevalence of
osteopenia in each group. The minimum odds ratio to be detected was
assumed to be 2.0, alpha was set to 0.05 and power (1-Beta) to 90% (see
Table 2-1). Calculations were performed using the method described by
Fleiss (1981) using Epi Info. The comparison group consists of
non-native people served by the Indian Health Service and/or the native
corporations. These health care systems serve relatively few
non-natives. The non-natives they do serve are mostly clinic staff and
their dependants. Given the small number of non-native people served by
the I.H.S. and the focus of this investigation on Alaska Natives, I
assumed that 1/3 of the subjects would be non-natives. These results led
to the choice of a t-score of --1.0 as the cutoff for a normal value.

[]{#__RefHeading___Toc502850834 .anchor}Table 2-1 Estimated Sample Size
Given Different Assumptions of Prevalence and Bone Density Cut Point for
Alaska Natives vs. Non-Natives

+-----------+----------+--------+--------+--------+----------+-------+
| **C       | \#       | **Na   | **Non- | **Est  | **E      | **    |
| riteria** | Non      | tive** | Native | imated | stimated | Total |
|           | -Native: |        | Preval | \#     | #Non-    | Subje |
|           |          | **     | ence** | Na     | Native** | cts** |
|           | **#      | Preval |        | tive** |          |       |
|           | Native** | ence** |        |        |          |       |
+-----------+----------+--------+--------+--------+----------+-------+
| t-score   | 1:2      | 0.44   | 0.21   | 129    | 64       | 193   |
| \<= -  |          |        |        |        |          |       |
+-----------+----------+--------+--------+--------+----------+-------+
| t-score   | 1:2      | 0.10   | 0.005  | 85     | 170      | 255   |
| \<= -2.5  |          |        |        |        |          |       |
|           |          |        |        |        |          |       |
| low       |          |        |        |        |          |       |
| p         |          |        |        |        |          |       |
| revalence |          |        |        |        |          |       |
+-----------+----------+--------+--------+--------+----------+-------+
| t-score   | 1:2      | 0.10   | 0.05   | 368    | 735      | 1,103 |
| \<= -2.5  |          |        |        |        |          |       |
| high      |          |        |        |        |          |       |
| p         |          |        |        |        |          |       |
| revalence |          |        |        |        |          |       |
+-----------+----------+--------+--------+--------+----------+-------+

Alpha = 0.05, Beta = 0.10, Minimum Detectable Odds Ratio Change = 2.0

### Univariate Analysis

I used SPSS 9.0 (SPSS, Inc., Chicago, IL) and Excel 97 (Microsoft, Inc.,
Redmond, WA) to perform the statistical analysis. The major variables of
interest are shown in Table 2-2. Bone density was dichotomized into low
density (t-score \<-1.0) and high density (t-score -1.0). Dichotomous
variables were examined by cross tabulation with normal vs. abnormal
bone density. Likelihood ratio Chi square statistics and odds ratios
(OR's) were calculated for each of these variables. For continuous
variables I generated descriptive statistics and histograms. In some
cases I assessed the normality of continuous variables with a
Kolmogorov-Smirnov test.

### Regression Modeling

I developed a set of logistic regression models that dichotomized
subjects by bone density to high density and low density using the same
criteria used in the univariate analysis. I used a method, modeled after
that of Hosmer and Lemeshow (1989), to select variables for the
multivariate analysis. The univariate likelihood ratio Chi squared
results were used to screen potential variables in the model. The
p-value for these tests had to be 0.25 or less unless the variable was
known from other sources to have a relationship with osteoporosis. These
potential variables are shown in Table 2-3. I used the gamma statistic
to measure the relatedness of binary variables with one another.
Interactions were tested using both forward and backward stepwise
likelihood ratio procedures. I assessed the fit of the model by plotting
the predicted probabilities from the final models against Cook's
distance and separately plotting predicted probabilities against the
Pearson residuals (Appendix C). Three individual cases which were
outliers on these plots and which changed the estimated odds ratios of
the model were removed from the final analysis. I also used the Hosmer
and Lemeshow goodness of fit test and Nagelkerke R^2^ as formal tests of
goodness of fit.

[]{#__RefHeading___Toc502850835 .anchor}Table 2-2 Candidate Predictor
Variables and Variable Type, Tested for Use as Covariates in the
Statistical Multivariate Analysis

+-----------------------------------------------------+----------------+
| **Predictor**                                       | **Variable     |
|                                                     | Type (units)** |
+-----------------------------------------------------+----------------+
| Dietary Calcium Intake from Dairy Products and      | Continuous     |
| Supplements                                         | (mg/day)       |
+-----------------------------------------------------+----------------+
| Calcium Supplementation                             | Dichotomous    |
+-----------------------------------------------------+----------------+
| Clinic Staff vs. Non-Staff                          | Dichotomous    |
+-----------------------------------------------------+----------------+
| Native vs. Non-Native                               | Dichotomous    |
+-----------------------------------------------------+----------------+
| History of Ankle Fracture                           | Dichotomous    |
+-----------------------------------------------------+----------------+
| Physical Exercise of at Least Twenty Minutes per    | Dichotomous    |
| Day 3x per Week                                     |                |
+-----------------------------------------------------+----------------+
| Oral Corticosteroids for at Least Six Weeks at Any  | Dichotomous    |
| Time in Their Life                                  |                |
+-----------------------------------------------------+----------------+
| Ever having Smoked Tobacco, Currently Smoking       | Dichotomous    |
| Tobacco,                                            |                |
|                                                     |                |
| Not ever Smoking Tobacco                            |                |
+-----------------------------------------------------+----------------+
| Cigarette Consumption History                       | Continuous     |
|                                                     | (packs/day and |
|                                                     | pack years)    |
+-----------------------------------------------------+----------------+
| The use of Hormone Replacement Therapy              | Dichotomous    |
+-----------------------------------------------------+----------------+
| The Use of Oral Contraceptive Pills                 | Dichotomous    |
+-----------------------------------------------------+----------------+
| Presence of Diabetes                                | Dichotomous    |
+-----------------------------------------------------+----------------+
| Anchorage vs. Other Sites                           | Dichotomous    |
+-----------------------------------------------------+----------------+
| Age at Time of Study Measurement                    | Continuous     |
|                                                     | (years)        |
+-----------------------------------------------------+----------------+

# Chapter 3 -- Results

Subjects took between 15-60 minutes to complete the study interview and
density measurement. I interviewed and measured a total of 483 potential
subjects, of those 452 (94%) met all inclusion criteria and provided
complete data for the purposes of statistical analysis. The geographical
distribution of study subjects is shown in Table 3-1. Sixteen persons
declined to participate in the study. Three persons declined because
they felt they were too young to have a bone density measurement, three
others declined because they did not want to answer study questions,
three did not want to find out if they had cancer, and two did not wish
to potentially lose their insurance coverage. The remaining five
declined to give a reason for not participating in the study.

I reviewed the medical records of 401 (89%) subjects approximately nine
months after the last study visit. Two persons were deceased at the time
of the review. There were 17 errors in birth dates found in the 401
birth dates reviewed. I personally entered all of the data, so my error
"rate" per keystroke, at 6 keystrokes per birth date, was 0.8%. Only
five of these erroneous birth dates differed by a year or more from the
correct birth date. Three subjects were taking alendronate, four
tamoxifen, and one calcitonin. The medical record agreed closely with
the questionnaire data. The study questionnaire identified diabetics
correctly 98% of the time, oral steroid users 93% of the time, and
hormone replacement therapy users 82% of the time.

The primary study analysis considered risk factors for low bone mass in
Alaska Natives (N=316). Risk factors for osteoporosis were highly
prevalent in the study population (Table 3-2). Smokers made up 35% of
all subjects and 45% of Alaska Natives. Nearly one in five Alaska
Natives reported having broken a foot or ankle at some time in their
lives. Ex-smokers comprised 30% of all subjects and 32% of Alaska
Natives. The median tobacco consumption history was eight pack-years.
Most Alaska Native participants (60%) had started menopause and 9% had
taken at least one six week course of oral steroids during their
lifetimes. Some protective factors were also prevalent in the study
population. Approximately one third of the Alaska Natives were taking
hormone replacement therapy, and 49% of Alaska Natives and 61% of
non-natives stated that they exercised at least three times a week for
20 minutes or more per day.

The median total calcium intake of Alaska Natives was 379 mg/day, and
63% of Alaska Natives reported that they took some type of dietary
supplement containing calcium. The calcium consumption of all subjects
combined was higher (Table 3-3) than that of Alaska Natives alone. This
was due to much higher calcium consumption by non-native study
participants.

Low bone density was highly prevalent in the study population, as 45% of
Alaska Natives and 22% of non-natives had a t-score less than ‑1.0. The
mean t-score of Alaska Natives was ‑0.9 compared to an average t-score
of +0.2 for non-natives (Table 3-4).

Many of the risk factors were significantly associated with low bone
density in the univariate analysis. Crude odds ratios for Alaska Natives
and for all subjects are shown in Table 3-5. Fracture history, steroid
use, menopause, hormone replacement therapy, current smoking, former
smoking, and age were all positively associated with low bone density
for Alaska Natives. Exercising three times per week for at least 20
minutes was negatively associated with low bone mass. Variables, which
did not pass the screening criteria for the multivariate model, were 1)
clinic staff vs. non-staff, 2) diabetics vs. non-diabetics, and 3)
Anchorage vs. all other sites.

Gamma coefficients for selected variables are shown in Tables 3-6 and
3-7. Diabetes was strongly associated with the subject having started
menopause (gamma = 0.68). The use of hormone replacement therapy was
also strongly related with menopausal status (gamma=1.0). No other
variables were strongly related with one another.

Several risk factors were significantly associated with low bone density
in the multivariate model (Table 3-8). In the analysis of Alaska
Natives, both current smokers and former smokers were more likely to
have low bone density compared to never smokers. Oral steroid use was
also associated with low bone density (Table 3-8).

The multivariate analysis of all subjects combined showed similar
results (Table 3-9). Smoking history and oral steroid use were less
strongly associated with low bone density than in univariate models. A
composite variable created by combining, history of hip, ankle, or foot
fracture, and history of osteoporosis diagnosis, was significantly
associated with low bone density. Alaska Natives were almost twice as
likely to have low bone density than non-natives (Table 3-9).

I eliminated several variables from the multivariate analysis during
statistical screening. Variables were screened to test a theoretical
relationship between the variable and bone density, or to test for a
confounding effect. Diabetes was evaluated because several of the field
visits were made with the diabetes team. As a result, diabetics were
over represented in the study. Diabetic status did not meet the
statistical screening criteria for inclusion in the model; I concluded
it was not an important source of confounding and eliminated it from the
multivariate analysis. Two other variables were eliminated from the
multivariate analysis by this process, being a health care worker versus
all other subjects, and being a subject in Anchorage versus other sites.
Hip fracture, foot fracture, and history of osteoporosis diagnosis were
rare enough events that I chose to combine them into a single variable
in the primary analysis.

The variables that passed the initial screening were included in the
initial multivariate logistic regression models. I included several
theoretically important variables in the final multivariate model even
though they were not statistically significant in that model. Hormone
replacement therapy use and menopause were highly related. When two
strongly correlated variables are included in the same multivariate
statistical model they will often dilute each other's association with
the dependant variable. Normally two variables that are this strongly
correlated would not be placed in the same multivariate model. In this
case, they are both important factors in explaining the risk for low
bone density. Hormone replacement is positively associated with low bone
mass in the univariate analysis. This is true in the multivariate
analysis until age and/or menopause are added to the model.

The results from the final logistic regression model for Alaska Natives
are shown in Table 3-8. All of the variables in this model were tested
for interactions with age. The age-steroid interaction was significant.
The age-smoking and age-history of fracture interactions approached
significance. I performed fit diagnostics as described in the methods
section, on a model with all of the variables in Table 3-9, and the
age-steroid interaction term. I removed three cases that were highly
influential. With the removal of these cases, the age-steroid term
became unambiguously non-significant (p=0.4), and the age-smoking term
became statistically significant. Goodness of fit diagnostics with the
age-smoking term was not as satisfactory. The Pearson residuals
demonstrated greater variance (Figure A-4). I decided that the best fit
and most interpretable model was a model without interaction terms. It
is likely that effect modification exists between two or more variables
in the model but the study sample was too small to draw meaningful
conclusions about the existence of specific relationships. Estimated
odds ratios calculated using the logistic regression model shown in
Table 3-8 are shown in Table 3-10.

[]{#__RefHeading___Toc502850837 .anchor}Table 3-1 Distribution of Study
Subjects by Study Site

  ------------- ----------------- -------------- ------------------- ------------
  **Site**      **Number of       **Percent of   **Population^1^**   **Percent
                Subjects**        Total                              Population
                                  Subjects**                         Sampled**

  Anchorage     184               41             258,752             0.07

  Barrow        38                8              4,397               0.86

  Bethel        3                 1              5,463               0.05

  Chenega Bay   10                2              35                  29

  King Cove     7                 2              703                 1

  Kodiak        23                5              6,859               0.34

  Kotzabue      21                5              2,964               0.71

  Nanwalek      20                4              180                 11

  Port Graham   31                7              190                 16

  Sand Point    18                4              830                 2

  Seward        64                14             3,040               2

  St. Paul      19                4              761                 3

  Tatitlek      14                3              110                 13

  Total         452               100            284,284             0.16
  ------------- ----------------- -------------- ------------------- ------------

^1^Estimate from Williams, 1999

[]{#__RefHeading___Toc502850836 .anchor}Table 3-2 Selected
Characteristics of Study Participants

+---------------------------+-----------+-------+-----------+--------+
| **Characteristic**        | **All     | **Nat | **Non     | **     |
|                           | S         | ive** | -Native** | P-valu |
|                           | ubjects** |       |           | e^1^** |
|                           |           | **N=  | **N=136** |        |
|                           | **N=452** | 316** |           |        |
+---------------------------+-----------+-------+-----------+--------+
| Current Smokers           | 35.2%     | 45.3% | 11.8%     | \<0    |
|                           |           |       |           | .001\* |
+---------------------------+-----------+-------+-----------+--------+
| Former Smokers            | 30.3%     | 31.6% | 27.2%     | 0.343  |
+---------------------------+-----------+-------+-----------+--------+
| History of Ankle or Foot  | 18.4%     | 19.0% | 16.9%     | 0.599  |
| fracture                  |           |       |           |        |
+---------------------------+-----------+-------+-----------+--------+
| Oral Steroid Use          | 8.2%      | 8.9%  | 7.4%      | 0.592  |
+---------------------------+-----------+-------+-----------+--------+
| Started Menopause         | 58.9%     | 60.4% | 55.2%     | 0.295  |
+---------------------------+-----------+-------+-----------+--------+
| Hormone Replacement       | 33.8%     | 31.3% | 39.7%     | 0.086  |
| Therapy                   |           |       |           |        |
+---------------------------+-----------+-------+-----------+--------+
| Exercise 3x/week\>=20min  | 52.8%     | 49.4% | 61.0%     | 0      |
|                           |           |       |           | .022\* |
+---------------------------+-----------+-------+-----------+--------+
| Diabetic                  | 10.0%     | 10.4% | 5.9%      | 0.108  |
+---------------------------+-----------+-------+-----------+--------+
| History of Hip Fracture   | 1.5%      | 1.9%  | 0.7%      | 0.325  |
+---------------------------+-----------+-------+-----------+--------+
| Clinic Staff              | 35.2%     | 20.3% | 69.9%     | \<0    |
|                           |           |       |           | .001\* |
+---------------------------+-----------+-------+-----------+--------+
| Low Bone Density          | 30.3%     | 45.3% | 22.1%     | \<0    |
|                           |           |       |           | .001\* |
+---------------------------+-----------+-------+-----------+--------+
| Calcium Supplement Use    | 58.0%     | 56.0% | 62.5%     | 0.198  |
+---------------------------+-----------+-------+-----------+--------+
| History of Hip, or Ankle  | 24.6%     | 26.0% | 21.3%     | 0.290  |
| or Foot Fracture, or      |           |       |           |        |
| Osteoporosis Diagnosis    |           |       |           |        |
+---------------------------+-----------+-------+-----------+--------+

\*Significant for p\<0.05

^1^Native Compared with Non-native Using Likelihood Ratio Chi Square
Test

[]{#__RefHeading___Toc502850839 .anchor}Table 3-3 Dietary Calcium
Measures (mg/day) for All Subjects and for Alaska Natives

+------------------------+-----------------+-----------------+---------+
| **Calcium Measure**    | **All           | **Alaska        | **p-val |
|                        | Subjects**      | Natives**       | ue^1^** |
|                        |                 |                 |         |
|                        | **Mean**        | **Mean**        |         |
|                        |                 |                 |         |
|                        | **Median**      | **Median**      |         |
|                        |                 |                 |         |
|                        | **(25-75        | **(25-75        |         |
|                        | percentile)**   | percentile)**   |         |
+------------------------+-----------------+-----------------+---------+
| Dairy Calcium          | 231             | 185             | \<      |
|                        |                 |                 | 0.001\* |
|                        | 140             | 107             |         |
|                        |                 |                 |         |
|                        | (39-359)        | (24-278)        |         |
+------------------------+-----------------+-----------------+---------+
| Total Dietary Calcium  | 360             | 306             | \<      |
|                        |                 |                 | 0.001\* |
|                        | 272             | 198             |         |
|                        |                 |                 |         |
|                        | (110-529)       | (94-441)        |         |
+------------------------+-----------------+-----------------+---------+
| Calcium from           | 309             | 298             | 0.028\* |
| Supplements            |                 |                 |         |
|                        | 0               | 0               |         |
|                        |                 |                 |         |
|                        | (0-257)         | (0-200)         |         |
+------------------------+-----------------+-----------------+---------+
| Total Calcium Intake   | 669             | 604             | \<      |
|                        |                 |                 | 0.001\* |
|                        | 471             | 379             |         |
|                        |                 |                 |         |
|                        | (191-916)       | (161-746)       |         |
+------------------------+-----------------+-----------------+---------+

\* p\<0.05

^1^Mann-Whitney U test, Alaska Natives vs. Non-Natives

[]{#__RefHeading___Toc502850838 .anchor}Table 3-4 Bone Density
Characteristics and Pack Years of Smoking for Study Subjects

  ------------------------ ------------ ------------- ---------------- --------------
  **Characteristic**       **All        **Alaska      **Non-native**   **p-value**
                           Subjects**   Natives**                      

  Age (years)              4912         5013          519              0.209^1^

  t-score                  -0.61.2      -0.91.2       0.21.2           \<0.001\*^1^

  Bone Mineral Density     0.5070.136   0.4830.137    0.5950.133       \<0.001\*^1^
  (g/cm^2^)                                                            

  Median Pack-Year Smoking 4            8             0                \<0.001\*^2^
  History                                                              
  ------------------------ ------------ ------------- ---------------- --------------

^\*^ p \<0.05

^1^Independent Samples t-test

^2^Mann-Whitney U test

[]{#__RefHeading___Toc502850840
.anchor}^Table\ 3-5\ Univariate\ Analysis\ of\ Subject\ Characteristics\ Tabulated\ by\ Abnormal\ vs.\ Normal\ Bone\ Density^1

+-----------------------+-------------+---------+------------+-------+
| **Variable**          | **All       | **All   | **Alaska   | **A   |
|                       | Subjects**  | Sub     | Native**   | laska |
|                       |             | jects** |            | Nat   |
|                       | **Crude     |         | **Crude    | ive** |
|                       | OR**        | **2     | OR**       |       |
|                       |             | sided** |            | **2   |
|                       |             |         |            | si    |
|                       |             | **p-    |            | ded** |
|                       |             | value** |            |       |
|                       |             |         |            | *     |
|                       |             |         |            | *p-va |
|                       |             |         |            | lue** |
+-----------------------+-------------+---------+------------+-------+
| Alaska Native vs.     | 2.9         | \<      | N/A        | N/A   |
|                       | (1.8-4.6)   | 0.001\* |            |       |
| Non-Native            |             |         |            |       |
+-----------------------+-------------+---------+------------+-------+
| History of Foot or    | 1.7         | 0.042\* | 2.3        | 0.    |
|                       | (1.0-2.7)   |         | (1.3-4.1)  | 005\* |
| Ankle Fracture        |             |         |            |       |
+-----------------------+-------------+---------+------------+-------+
| Exercise at Least 3x  | 0.5         | 0.001\* | 0.6        | 0.    |
| per Week              | (0.4-0.8)   |         | (0.4-1.0)  | 016\* |
+-----------------------+-------------+---------+------------+-------+
| Diabetic              | 1.4         | 0.270   | 1.3        | 0.446 |
|                       | (0.8-2.7)   |         | (0.6-2.7)  |       |
+-----------------------+-------------+---------+------------+-------+
| Oral Steroid Use      | 1.9         | 0.061\* | 2.8        | 0.    |
|                       | (1.0-3.7)   |         | (1.2-6.4)  | 012\* |
+-----------------------+-------------+---------+------------+-------+
| Started Menopause     | 3.7         | \<      | 4.3        | \<0.  |
|                       | (2.4-5.6)   | 0.001\* | (2.7-7.0)  | 001\* |
+-----------------------+-------------+---------+------------+-------+
| Hormone Replacement   | 1.4         | 0.085\* | 1.8        | 0.    |
| Therapy               | (1.0-2.1)   |         | (1.1-3.0)  | 013\* |
+-----------------------+-------------+---------+------------+-------+
| Calcium Supplement    | 1.1         | 0.593   | #### 1.4   | ##    |
| Use                   | (0.8-1.6)   |         |  (0.9-2.3) | ## 0. |
|                       |             |         |            | 174\* |
+-----------------------+-------------+---------+------------+-------+
| History of Hip        | 10.0        | 0.009\* | 6.2        | 0.    |
| Fracture              | (1.2-84.0)  |         | (0.8-54.0) | 051\* |
+-----------------------+-------------+---------+------------+-------+
| Clinic Staff vs.      | 0.6         | 0.002\* | 0.8        | 0.404 |
| Non-Staff             | (0.4-0.8)   |         | (0.5-1.4)  |       |
+-----------------------+-------------+---------+------------+-------+
| Current Smoker vs.    | 2.6         | \<      | 2.3        | 0.    |
|                       | (1.7-4.3)   | 0.001\* | (1.3-4.3)  | 005\* |
| Never Smoker          |             |         |            |       |
+-----------------------+-------------+---------+------------+-------+
| Former Smoker vs.     | 2           | 0.004\* | 2.2        | 0.    |
|                       | .1(1.3-3.4) |         | (1.2-4.2)  | 012\* |
| Never Smoker          |             |         |            |       |
+-----------------------+-------------+---------+------------+-------+
| History of            | 3.4         | 0.004\* | 2.8        | 0.    |
| Osteoporosis          | (1.4-8.3)   |         | (1.0-7.5)  | 036\* |
| Diagnosis             |             |         |            |       |
+-----------------------+-------------+---------+------------+-------+
| History of Hip,       | 2.3         | \<      | 2.7        | \<0.  |
| Ankle, Foot Fracture, | (1.5-3.5)   | 0.001\* | (1.6-4.6)  | 001\* |
| or                    |             |         |            |       |
|                       |             |         |            |       |
| Osteoporosis          |             |         |            |       |
| Diagnosis             |             |         |            |       |
+-----------------------+-------------+---------+------------+-------+
| Anchorage vs.         | 1.2         | 0.383   | 1.0        | 0.969 |
|                       | (0.8-1.8)   |         | (0.6-1.6)  |       |
| All Other Sites       |             |         |            |       |
+-----------------------+-------------+---------+------------+-------+
| Age in 5 Year         | 1.4         | \<      | 1.4        | \<0.  |
| Intervals^2^          | (1.3-1.5)   | 0.001\* | (1.3-1.6)  | 001\* |
+-----------------------+-------------+---------+------------+-------+
| Calcium Intake in mg  | #### 1.     | ###     | 1.0        | 0.    |
|                       | 0 (1.0-1.0) | # 0.438 | (1.0-1.0)  | 061\* |
+-----------------------+-------------+---------+------------+-------+
| Pack-Years of Smoking | #### 1.     | \<      | 1.3        | \<0.  |
| History               | 3 (1.2-1.4) | 0.001\* | (1.1-1.4)  | 001\* |
+-----------------------+-------------+---------+------------+-------+

\*p0.250 Screening Level for Multivariate Model

^1^Abnomal Bone Density is Defined as a t-score of --1.0 or Less by
Calcaneal Ultrasound

^2^Age is Modeled as the Number of 5 Year Intervals from Age 30

Table 3-6 Gamma^1^ Coefficients Among Selected Study Variables as an
Evaluation of Relatedness of Candidate Predictor Variables in a
Multivariate Analysis for Alaska Native Subjects Only

+----------------+-----------------+----------------+----------------+
| **Variable**   | Started         | **Smoking      | **Hormone      |
|                | Menopause       | Status^3^      | Replacement    |
|                |                 | Gamma          | Therapy**      |
|                | **Gamma         | (p-value)**    |                |
|                | (p-value)**     |                | **Gamma        |
|                |                 |                | (p-value)**    |
+----------------+-----------------+----------------+----------------+
| Started        | N/A             | 0.062(0.525)   | 1.0            |
| Menopause      |                 |                | (\<0.001)\*^2^ |
+----------------+-----------------+----------------+----------------+
| Hormone        | 1.0             | 0.054(0.577)   | N/A            |
| Replacement    | (\<0.001)\*^2^  |                |                |
| Therapy        |                 |                |                |
+----------------+-----------------+----------------+----------------+
| Oral Steroid   | 0.006(0.975)    | -0.159(0.348)  | 0.268(0.204)   |
| Use            |                 |                |                |
+----------------+-----------------+----------------+----------------+
| Exercise at    | 0.076(0.491)    | -0.034(0.701)  | 0.250(0.029)   |
| Least 3x per   |                 |                |                |
| Week           |                 |                |                |
+----------------+-----------------+----------------+----------------+
| Smoking        | 0.062(0.525)    | N/A            | 0.054(0.577)   |
| Status^3^      |                 |                |                |
+----------------+-----------------+----------------+----------------+
| Clinic Staff   | 0.013(0.928)    | -0.343(0.002)  | 0.172(0.250)   |
| vs.            |                 |                |                |
|                |                 |                |                |
| Non-Staff      |                 |                |                |
+----------------+-----------------+----------------+----------------+
| History of     | 0.499(\<0.001)  | 0.219(0.044)   | 0.296(0.028)   |
| Hip, Ankle,    |                 |                |                |
| Foot Fracture, |                 |                |                |
| or             |                 |                |                |
| Osteoporosis   |                 |                |                |
| Diagnosis      |                 |                |                |
+----------------+-----------------+----------------+----------------+
| Diabetes       | 0               | -0.312(0.018)  | 0.264(0.178)   |
|                | .688(\<0.001)\* |                |                |
+----------------+-----------------+----------------+----------------+
| Calcium        | 0.160(0.164)    | 0.078(0.412)   | 0.166(0.171)   |
| Supplement Use |                 |                |                |
+----------------+-----------------+----------------+----------------+
| Anchorage vs.  | -0.224(0.065)   | 0.293(0.002)   | -0.227(0.075)  |
|                |                 |                |                |
| All Other      |                 |                |                |
| Sites          |                 |                |                |
+----------------+-----------------+----------------+----------------+

\*Highly Related (gamma \> 0.6)

^1^Gamma is Used to Compare the Relatedness of Binary Variables and is
Interpreted Like a Correlation Coefficient

^2^2x2 Table has One Empty Cell Because No Premenopausal Women were
Taking Hormone Replacement Therapy

^3^Smoking Status Coded as an Ordinal Variable; 0=Never Smoker, 1=Former
Smoker, 2=Current Smoker

[]{#__RefHeading___Toc502850842 .anchor}Table 3-7 Gamma^1^ Coefficients
Amoung Selected Study Variables as an Evaluation of Relatedness of
Candidate Predictor Variables in a Multivariate Analysis for All Study
Subjects

+-------------------+--------------+---------------+------------------+
| **Variable**      | **Started    | **Smoking     | **Hormone        |
|                   | Menopause**  | Status^3^     | Replacement      |
|                   |              | Gamma         | Therapy**        |
|                   | **Gamma      | (p-value)**   |                  |
|                   | (p-value)**  |               | **Gamma          |
|                   |              |               | (p-value)**      |
+-------------------+--------------+---------------+------------------+
| Started Menopause | N/A          | 0.126(0.108)  | 1.00             |
|                   |              |               | (\<0.001)\*^2^   |
+-------------------+--------------+---------------+------------------+
| Hormone           | 1.00(\       | -0.010(0.896) | N/A              |
| Replacement       | <0.001)\*^2^ |               |                  |
| Therapy           |              |               |                  |
+-------------------+--------------+---------------+------------------+
| Oral Steroid Use  | 0.038(0.825) | -0.120(0.401) | 0.301(0.089)     |
+-------------------+--------------+---------------+------------------+
| Exercise at Least | -0.194       | -0.186(0.014) | -0.097(0.330)    |
| 3x per Week       | (0.040)      |               |                  |
+-------------------+--------------+---------------+------------------+
| Smoking Status^3^ | 0.126(0.108) | N/A           | -0.10(0.896)     |
+-------------------+--------------+---------------+------------------+
| Clinic Staff vs.  | -0.051       | -0            | -.305(0.002)     |
| Non-Staff         | (0.608)      | .507(\<0.001) |                  |
+-------------------+--------------+---------------+------------------+
| History of Hip,   | 0.362        | 0.120(0.191)  | 0.239(0.036)     |
| Ankle, Foot       | (0.001)      |               |                  |
| Fracture, or      |              |               |                  |
| Osteoporosis      |              |               |                  |
| Diagnosis         |              |               |                  |
+-------------------+--------------+---------------+------------------+
| Alaska Native vs. | 0.108        | 0.6           | -0.181(0.091)    |
|                   | (0.297)      | 46(\<0.001)\* |                  |
| Non-Native        |              |               |                  |
+-------------------+--------------+---------------+------------------+
| Diabetes          | 0.63         | -0.085(0.440) | 0.282(0.099)     |
|                   | 9(\<0.001)\* |               |                  |
+-------------------+--------------+---------------+------------------+
| Calcium           | 0.127(0.188) | -0.051(0.513) | 0.149(0.137)     |
| Supplement Use    |              |               |                  |
+-------------------+--------------+---------------+------------------+
| Anchorage vs.     | -            | 0             | -0.214(0.310)    |
|                   | 0.221(0.021) | .302(\<0.001) |                  |
| All Other Sites   |              |               |                  |
+-------------------+--------------+---------------+------------------+

\*Highly Related (gamma \> 0.6)

^1^Gamma is Used to Compare the Relatedness of Binary Variables and is
Interpreted Like a Correlation Coefficient

^2^2x2 Table has One Empty Cell Because no Premenopausal Women were
Taking Hormone Replacement Therapy

^3^Smoking Status Coded as an Ordinal; Variable 0=Never Smoker, 1=Former
Smoker, 2=Current Smoker

[]{#__RefHeading___Toc502850843 .anchor}Table 3-8 Predictors of Low Bone
Density from a Multiple Logistic Regression Model for Alaska Native
Subjects Only

  ------------------------------------------------ -------------- ----------------
  **Variable**                                     **Adjusted     **p-value^1^**
                                                   OR^2^**        

  Current Smoker                                   3.9(1.8-8.4)   \<0.001\*

  Former Smoker                                    2.8(1.3-6.2)   0.010\*

  History of Hip, Ankle, Foot Fracture, or         1.7(1.0-3.1)   0.082
  Osteoporosis Diagnosis                                          

  Age (5 year Increase)^3^                         1.5(1.3-1.8)   \<0.001\*

  Hormone Replacement Therapy                      0.6(0.3-1.1)   0.119

  Started Menopause                                1.4(0.6-3.1)   0.442

  Oral Steroid Use                                 4.7(1.8-12)    0.001\*

  Exercise at Least 3x per Week                    0.7(0.4-1.2)   0.184
  ------------------------------------------------ -------------- ----------------

^\*^p\<0.05

^1^p-value by Wald Test

^2^All Variables Listed in the Table are Included in the Multiple
Logistic Regression Model

^3^Age is Modeled as the Number of 5 Year Intervals from Age 30

[]{#__RefHeading___Toc502850844 .anchor}Table 3-9 Predictors of Low Bone
Density from a Multiple Logistic Regression Model for Non Native and
Alaska Native Study Subjects

  ------------------------------------------------ -------------- ----------------
  **Variable**                                     **Adjusted     **p-value^1^**
                                                   OR^2^**        

  Current Smoker                                   2.3(1.3-4.2)   0.004\*

  Former Smoker                                    1.6(1.0-2.9)   0.103

  History of Hip, Ankle, Foot Fracture, or         1.7(1.0-2.8)   0.043\*
  Osteoporosis Diagnosis                                          

  Age (5 year Increase)^3^                         1.4(1.2-1.6)   \<0.001\*

  Hormone Replacement Therapy                      0.6(0.3-1.0)   0.051

  Started Menopause                                1.7(0.8-3.3)   0.148

  Oral Steroid Use                                 2.2(1.1-4.8)   0.037\*

  Exercise at Least 3x per Week                    0.7(0.4-1.0)   0.056

  Alaska Native vs. Non-native                     1.8(1.1-3.1)   0.024\*
  ------------------------------------------------ -------------- ----------------

^\*^p\<0.05

^1^p-value by Wald Test

^2^All Variables Listed in the Table are Included in the Multiple
Logistic Regression Model

^3^Age is Modeled as the Number of 5 Year Intervals from Age 30

[]{#__RefHeading___Toc502850845 .anchor}Table 3-10 Low Bone Density by
Age Group for Alaska Natives

+-------------------------------+--------------------------------------+
| Age group (Years)             | ###### Adjusted OR^1^                |
+-------------------------------+--------------------------------------+
| 20-24                         | 0.4                                  |
+-------------------------------+--------------------------------------+
| 25-29                         | 0.7                                  |
+-------------------------------+--------------------------------------+
| 30-34^2^                      | 1.0                                  |
+-------------------------------+--------------------------------------+
| 35-39                         | 1.5                                  |
+-------------------------------+--------------------------------------+
| 40-44                         | 2.3                                  |
+-------------------------------+--------------------------------------+
| 45-49                         | 3.4                                  |
+-------------------------------+--------------------------------------+
| 50-54                         | 5.1                                  |
+-------------------------------+--------------------------------------+
| 55-59                         | 7.8                                  |
+-------------------------------+--------------------------------------+
| 60-64                         | 12                                   |
+-------------------------------+--------------------------------------+
| 65-69                         | 18                                   |
+-------------------------------+--------------------------------------+

^1^Adjusted for Smoking History, Exercise, Menopausal Status, Hormone
Replacement Therapy Usage, Oral Steroid Use, and Fracture History

^2^Reference Group Age 30-34 Years

# Chapter 4 -- Discussion

Osteoporosis is a debilitating degenerative disease associated with
aging. Environmental, behavioral, and genetic factors act to either
decrease or increase the risk of osteoporosis. Several factors suggest
that Alaska Natives may have a different risk profile for osteoporosis
than the United States average. Changing behavior patterns among Alaska
Natives combined with an increasing life expectancy make this an
important and interesting population to evaluate for osteoporosis risk.

Alaska is exposed to considerably less sunlight than other parts of the
United States. Sunlight is required for the conversion of
7-dehydrocholesterol to previtamin D3, an essential component for
calcium absorption (Marcus, 1996). This conversion process is less
efficient in the elderly, adding to their risk for osteoporosis and
fractures (Heaney, 1996). Inefficient conversion combined with low
exposure to sunlight may have a profound effect on elderly Alaska
Natives. The changing diet of Alaska Native peoples may also increase
their risk for osteoporosis. The diet of indigenous peoples has changed
dramatically in the last 30 years (Whiting, 1998). The effect of these
dietary changes on calcium intake is not well characterized. Informally,
I found that Alaska Natives consume smaller quantities of milk and other
dairy products than the United States average. This low consumption is
due to the high prevalence of lactose intolerance, high cost, and
limited availability of these foods. Lactose intolerance is a special
problem, because the major food items fortified with vitamin D are dairy
foods. This combination of reduced dietary vitamin D intake and low
exposure to sunlight may lower bone density.

I measured an average calcium intake lower than reported by other
researchers. Nobmann and colleagues (Nobmann, 1992) found a mean dietary
intake, excluding dietary supplements, of 516 mg/day in Alaska Native
females and 597 mg/day for Caucasian females in the National Health and
Nutrition Examination Survey II (NHANES II). In this survey Alaska
Natives consumed 306 mg/day on average. When I included dietary
supplements in my sample, the discrepancy between my measurement and the
value reported by Nobmann et. al. disappeared. Cummings and colleagues
(Cummings, 1995) reported a higher mean calcium intake, excluding
supplements, of 713 mg/day in a sample of 9,516 Caucasian women. This
informal comparison supports the hypothesis that Alaska Natives have
lower calcium intake than Caucasian women. All of these estimates are
lower than the recommended dietary intake (1200 mg/day) (National
Osteoporosis Foundation, 1999). The differences between my results and
the work of others may be due to the use of different dietary measures.
My dietary survey did not include all high calcium foods. Notably absent
from the measurement was cheese, which can be a major source of dietary
calcium. It is unlikely, but possible, that including cheese could raise
the mean daily intake to the recommended level. The large difference
between measured and recommended intakes suggests that Alaska Natives
are not consuming recommended levels of calcium.

Hormone replacement therapy and menopause were related to one another
and to low bone density. This confounding relationship is easily
explained. Women taking hormone replacement therapy are older than the
study population as a whole. When either menopausal status or age is
added to the statistical model, this confounding with age is eliminated,
and hormone replacement therapy is then negatively associated with low
bone density. An additional complication is that premenopausal women do
not take hormone replacement therapy, almost by definition. This fact
made it impossible for me to test for statistical interactions between
these two important variables using conventional statistical techniques.

## Issues with Ultrasound Bone Density Technology

The diagnosis of osteoporosis is most often made with a bone density
measurement. While the interpretation of these tests seems simple,
several technical issues complicate their interpretation.

A variety of bone density measurement techniques are available (Lang,
1991). The most widely accepted technique is Dual Energy X-ray
Absorptiometry (DEXA). DEXA uses an X-ray source to generate two photon
beams of known intensities and energies. The beams are directed at a
specific anatomic site. A detector on the opposite side of the site
measures the amount of radiation that passes through the tissue. The
absorption of radiation roughly follows Beer's law (Barrow, 1988). Since
the intensity of the X-ray beam is known, it is an easy calculation to
determine how much radiation the bone absorbs. Denser bone absorbs more
radiation. The use of two photon energies with different absorption
characteristics allows the device to correct for the amount of soft
tissue surrounding the bone. This correction allows accurate
measurements of relatively deep anatomic sites such as the femoral neck
and lumbar vertebrae.

Ultrasound is a relatively new technology for bone density measurements.
Ultrasound methods pass high frequency sound waves through the anatomic
site of interest. Unlike DEXA, ultrasound cannot correct for the amount
of soft tissue around deep anatomic skeletal sites. Instead of the
femoral neck or lumbar vertebrae, shallow anatomic sites are measured.
The most common measurement sites are the patella, metacarpals, and the
calcaneus. Even when measuring at shallow sites, edema can affect the
measurement (Johansen, 1997). Ultrasound densitometry most commonly
takes advantage of two physical properties to make its measurements,
attenuation and speed of sound (Njeh, 1997). Broadband ultrasound
attenuation (BUA) is a measure of the absorption of sound by bone and is
analogous to X-ray absorption used by DEXA. Speed of sound through the
bone is often measured as well. Denser, healthier bone allows sound
waves to propagate at a higher speed than in lower density bone.

DEXA and ultrasound densitometry are strongly correlated measurements of
bone density. Greenspan and colleagues found correlation coefficients
between DEXA at the calcaneus and several ultrasound units of 0.79-0.86.
The relationship was slightly weaker between DEXA of the femoral neck
and ultrasound at the calcaneus (r = 0.7-0.8) (Greenspan, 1997).

While ultrasound is a valid technique, it has limitations. Ultrasound is
a less precise measurement technique than DEXA; therefore it has a
larger standard deviation. A t-score of -1.0 obtained by ultrasound is a
lower density than a t-score of -1.0 obtained by DEXA. This results in
systematic underdiagnoses of osteoporosis by ultrasound using W.H.O.
criteria (Faulkner, 1999).

The differences between DEXA and ultrasound are the reason I did not use
the ultrasound results as a diagnostic marker for osteoporosis. Using
the W.H.O. criteria, of a t-score less than ‑2.5, to diagnose
osteoporosis using calcaneal ultrasound misclassifies most people with
osteoporosis as non-osteoporotic. Theoretically, I could use a different
cut point that adjusts for the differences between ultrasound and DEXA.
No widely accepted method for this adjustment exists. I opted instead to
use a cutoff of -1.0 to define an abnormal result. This is an accepted
W.H.O. criterion for low bone density or osteopenia. Using this
criterion, almost all persons who have osteoporosis will have an
abnormal test result. The disadvantage of this method is that some
persons with low bone mass by DEXA will be misclassified as having
normal bone mass. The cut off of --1.0 strikes a reasonable balance
between detecting persons at high risk of low bone density and avoiding
large numbers of false positive results.

## Limitations

This study was a cross sectional study, so no information about the
temporal relationship between risk factors and bone density was
gathered. I selected subjects for the study by convenience sampling.
Subjects are persons using the health care system, and many of the
subjects have reasons to be concerned about osteoporosis. For example,
several subjects were referred to the study because of long term
corticosteroid use.

Communities were selected based on the level of support from the local
health care system and tribal corporations. These communities may differ
from the Alaska Native population as a whole in terms of availability of
health care resources or other factors that may affect risk factor
distribution. I did not visit any sites in Southeast Alaska, and I did
not obtain enough data from northern communities to test for a
relationship between average daylight exposure and bone density.

The calcium measure I used was, at best, a good estimate of dietary
sources of calcium. The instrument relied heavily on dairy products to
estimate calcium intake. This may not be appropriate because dairy
products are only recently widely available in rural Alaska and are
expensive. Many of the study subjects reported that they were lactose
intolerant. These factors imply that dairy calcium makes up a smaller
proportion of total dietary calcium than it does in the general United
States population. The dietary measure also did not account for the
significant seasonal variation in the diet of Alaska Natives (Nobmann,
1992). Foods like salmon, herring eggs, and salmon berries are available
only during a short season during the summer, although some are
preserved and consumed throughout the year. Exercise is also a seasonal
activity in Alaska. The exercise habits of subjects across the seasons
were not assessed in this study.

Misclassification of ethnicity is a concern in this study. I determined
the ethnicity of subjects by self-report. I did not identify blood
quantum. Some non-native subjects were possibly misclassified as Alaska
Natives. This leads to difficult questions about the definition of an
Alaska Native, as intermarriage between different tribes, and with
Caucasians, is quite common. I do not believe that this limitation
seriously compromises my results, because all subjects were recruited
from I.H.S. facilities. Patients at these facilities must demonstrate
some Alaska Native or American Indian heritage to receive care.

Alcohol abuse is a risk factor for osteoporosis that I did not attempt
to measure. Accurately assessing alcohol consumption is quite difficult
and complicates the patient interview. I decided that these difficulties
outweighed the benefits of collecting information about alcohol
consumption.

In this study, I used the odds ratio as my measure of association. This
measure is statistically valid, but the odds ratio does not approximate
the relative risk in this case because of the high prevalence of low
bone density in this population.

I personally interviewed the subjects. It is possible that I asked the
questions in such a manner as to bias the results. To minimize this bias
I followed the questionnaire closely and always performed the bone
density test after I completed the interview.

The sample size, while adequate to test for many main effects, was too
small to detect a statistically significant effect for exercise. A
larger sample would make it possible to more reliably test for effect
modification between age and the variables whose interaction terms
approached significance.

## Future Studies 

Several changes to this study design should be incorporated into future
work. A larger sample, with better representation of Southeast Alaska
and of native peoples living above the Arctic Circle, would allow richer
statistical models to be constructed. Measurement of body mass index,
serum vitamin D, and better assessment of comorbidities such as
rheumatoid arthritis, cancer, and seizure disorders would make it
possible to adjust statistically for these important factors. A large
enough sample with broad representation would allow for the development
of a reference database of bone densities for this population. A
measurement of the prevalence of lactose intolerant persons would be
useful. A further improvement would be to follow subjects as a cohort to
determine the rate of osteoporotic fractures in this population.

## Conclusions

Osteoporosis is a growing problem for Alaska Natives. My work indicates
that several risk factors for osteoporosis exist in this population. The
magnitude of the associations between risk factors is similar to that
seen by other investigators in cohort studies (Forsen, 1998; Cummings,
1995).

The total calcium consumption of Alaska Natives in this study is lower
than the USDA recommended level. Given the high prevalence of other risk
factors this is a cause for concern. These results suggest that a
population based dietary intervention is worthwhile. The dietary
intervention should not focus only on calcium intake; instead it should
be a comprehensive intervention that emphasizes a low fat and high fiber
diet.

Other modifiable risk factors should also be addressed in a prevention
program. Clearly, a high priority needs to be placed on smoking
cessation programs. The high prevalence of exercise in the study
population is encouraging and suggests that indoor community centers
that facilitate exercise during the winter will be utilized if built.
Programs for the elderly to prevent falls should also be implemented
where feasible. Prevention programs in Alaska need to account for the
large seasonal variation in diet and activity level.

The Alaska Native population is aging and becoming more at risk for
osteoporosis. Interventions exist which can decrease the risks of
developing osteoporosis for individuals and for whole populations.
Effective treatments for osteoporosis are available. Right now we have a
golden opportunity to implement a systematic program to prevent
osteoporosis and fractures. If we take advantage of this opportunity we
can avoid a significant amount of morbidity and mortality in Alaska
Natives.

# References

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# Appendix A -- Consent Form

**Title:** Assessment of Risk Factors for Osteoporosis Among Alaska
Natives

**Principle Investigators:** Kelly Krohn, M.D. (503) 494-8963

> Joshua Filner, Medical Student (907) 729-1126

**Co-investigator**: Jane Kelly, M.D. (907) 729-1126

**Purpose**: You have been invited to participate in this research study
because you are a

women over the age of twenty. The purpose of this research is to learn
about the risks

Alaska Native women have for osteoporosis, often called brittle bone
disease.

Participation takes about 15 minutes.

**Procedures**: If you decide to participate in this research study we
will ask you question

about what foods you eat and some medical conditions which are known to
affect the risk

of getting osteoporosis. Afterwards we will measure how strong your
bones are using a

machine approved by the Food and Drug Administration. This machine uses
ultrasound,

not x-rays. It takes a few minutes to set up and about 10 seconds to
measure your bone

strength. You will feel the machine buzz while it is working. You will
be given a copy

of the results and a copy of this form to take home with you. A doctor
or other health care practitioner will discuss any unusual results with
you. A copy or your results will be put in your medical record. We will
also check your medical record to see if you have taken certain
medicines or have certain illnesses known to affect the risk of
osteoporosis.

**Risks and Discomforts**: There are no known risks associated with the
use of this or any

ultrasound. The bone measurement is painless. Some ultrasound gel will
get on your

foot. The information we will collect could affect your insurability.

**Benefits:** You may or may not be personally helped by participating
in this study. By

participating in this study you are helping us understand who will get
osteoporosis and

help develop programs to prevent it. You also get a free ultrasound
measurement of your

bones which will tell you something about your chances of getting
osteoporosis.

**Alternatives**: You may choose not to participate in this study.

**Confidentiality**: Neither your name nor your identity will be used
for publication or

publicity purposes.

**Costs**: There is no cost for participation in this study.

**Liability**: The Oregon Health Sciences University, as a public
cooperation, is subject to

the Oregon Tort Claims Act, and self insured for liability claims. If
you suffer any injury

from this research project compensation would be offered to you only if
you establish

that the injury occurs through the fault of the University, its officers
or employees.

However, you have not waived any legal rights by signing this form. If
you have further

questions, please call the Oregon Health Sciences University Medical
Services Director

at (503) 494-6020 or Dr. David Barrett of the Alaska Native Medical
Center at (907)

229-2062. It is not the policy of the U.S. Department of Health and
Human Services, or

any federal funding the research project in which you are participating
to compensate or provide medical treatment for human subjects in the
event the research results in physical injury.

**Participation**: Dr. Kelly Krohn (503) 494-8963 has offered to answer
any other questions you may have about this study. If you have questions
regarding your rights as

a research subject you may contact the Oregon Health Sciences University
Institutional

Review Board at (503) 494-7887 or Dr. David Barrett of the Alaska Native
Medical

Center Institutional Review Board at (907) 229-2062. You may refuse to
participate or

you may withdraw from this study without affecting your relationship
with or treatment

at this clinic, Alaska Native Medical Center, or Oregon Health Sciences
University.

Your signature below indicates that you have read the foregoing and
agree to participate

in this study.

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ \_\_\_\_\_\_\_\_\_\_\_\_\_\_

Study Participant Date

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ \_\_\_\_\_\_\_\_\_\_\_\_\_\_

Investigator Date

# Appendix B -- The Study Questionnaire 

Native Alaskan Bone Mineral Density Pilot Project

IDENTIFYING INFORMATION

Id. Number \<idnum\> Date of Interview \<mm/dd\> Date Entered
\<today/yy\>

Last name \<A \> First name & initial \<A \>

City or town \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ ZIP \#####

Site \# ( 1= Anchorage 2= Sandpoint 3=Kodiak )

Date of Birth \<mm/dd/yy\> Age \### years Sex \<A\> (M/F)

Native American \<Y\> Tribe1\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Tribe2
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Have you ever broken your ankle or foot or had pins placed in them?
\<Y\>

BMD #.### T-Score ##.# BUA ##.# SOS ####.#

Dietary Questionnaire

Will Multiply servings/week by 4 for monthly

Food Do you eat Average Servings/week

Milk,fresh (1 cup) \<Y\> ##.#

Milk, Evaporated(1/4 cup) \<Y\> ##.#

Milk, powdered \<Y\> ##.#

(1/3 cup powder w/1 cup water = 1 cup milk)

Coffee w/milk \<Y\> ##.#

or cream (1 Tbsp.)

Yogurt, Plain 1 cup \<Y\> ##.#

Ice Cream (1/2 cup=scoop) \<Y\> ##.#

Canned Salmon w/bones (1/4 cup) \<Y\> ##.#

Sardines (1 can) \<Y\> ##.#

Fish Head soup (2 cups) \<Y\> ##.#

Stinkfish? (SERVING SIZE?) \<Y\> ##.#

Kelp w/herring eggs (1 cup) \<Y\> ##.#

Small blackfish w/bones (3 oz.) \<Y\> ##.#

Dried Salmon, Sockeye or Red(3 oz.)\<Y\> ##.#

Whole Needlefish ( 3 oz.) \<Y\> ##.#

Juices w/Calcium (1 cup) \<Y\> ##.#

Fishheads (2 heads) \<Y\> ##.#

Dark Green Leafy Vegetables \<Y\> ##.#

(1 cup)

Broccoli (2 spears) \<Y\> ##.#

Cold Cereal (1 1/2 cup) \<Y\> ##.#

Hot Cereal (1 cup) \<Y\> ##.#

Egg (1) \<Y\> ##.#

Do you take Ca supplements (e.g. tums, Oscal)? \<Y\>

If so how often/week \# Dose \#####

Do you do physical labor at home or as part of a paid job? \<Y\>

Do you exercise at least 3 times a week for 20 minutes or more? \<Y\>

Has a healthcare provider suggested exercise in the last year or last
visit? \<Y\>

Have you ever broken your hip? \<Y\>

Has a health care provider ever told you you have osteoporosis? \<Y\>

Do you have diabetes? \<Y\>

Are you taking or have you taken steroids(longer than 6 wks.)? \<Y\>

Do you or have you ever smoked tobacco? \<Y\> If so for how long?(years)
##.#

How many packs per day? #.#

Has any healthcare provider suggested quitting in the last year? \<Y\>

For women only:

Have you started menopause (have your periods stopped)? \<Y\>

Are you taking hormone replacement therapy? \<Y\>

Are you taking or have you ever taken birth control pills? \<Y\>

If so for how long?(Years) ##.#

Comments
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

# Appendix C -- Methods Notes

[]{#__RefHeading___Toc502850846 .anchor}Table A-1 Site Specific
Recruitment Notes

+------------+---------------------------------------------------------+
| **A        | In 1998 the investigator worked at several different    |
| nchorage** | clinics (Family Medicine, Internal Medicine, Women's    |
|            | Health and Orthopedics). Subjects were referred         |
|            | directly by providers working at that clinic.           |
|            |                                                         |
|            | In 1999, to fill a shortage of non-native subjects and  |
|            | make more efficient use of clinic time, appointments    |
|            | were scheduled in advance for many study subjects in    |
|            | addition to the 1998 methodology. Due to space          |
|            | constraints and organizational changes subjects were    |
|            | recruited only from the Orthopedics and the Women's     |
|            | Health clinics.                                         |
+------------+---------------------------------------------------------+
| **Bethel** | Very few subjects met the local institutional           |
|            | requirements for referral to the study. All subjects    |
|            | had a high degree of suspicion for osteoporosis         |
|            |                                                         |
|            | (1998 only).                                            |
+------------+---------------------------------------------------------+
| **Kotzabue | A traveling rheumatologist referred many of the         |
| Barrow**   | subjects to the study                                   |
|            |                                                         |
|            | (1999 only).                                            |
+------------+---------------------------------------------------------+
| **Sand     | At these sites walk-in patients were recruited for the  |
| Point**    | study; no phone calls were made for recruitment. These  |
|            | visits were made in conjunction with the diabetes team  |
| **Kodiak** | (1998 only).                                            |
+------------+---------------------------------------------------------+
| **Seward** | At these sites the majority of subjects were recruited  |
|            | via telephone calls from the local providers and had    |
| **         | appointments scheduled in advance. Walk in subjects     |
| Tatitlek** | were also inducted into the study.                      |
|            |                                                         |
| **         | (Seward 1998 and 1999, Others 1998 only)                |
| Nanwalek** |                                                         |
|            |                                                         |
| **Chenega  |                                                         |
| Bay**      |                                                         |
|            |                                                         |
| **Port     |                                                         |
| Graham**   |                                                         |
|            |                                                         |
| **King     |                                                         |
| Cove**     |                                                         |
+------------+---------------------------------------------------------+

[]{#__RefHeading___Toc502850847 .anchor}Table A-2 Elemental Calcium
Content of Food Items per Serving Used in Calculations of Dietary
Calcium Intake^1^

+----------------------------------------+-----------------------------+
| **Food**                               | ######                      |
|                                        | Calcium Content per Serving |
+----------------------------------------+-----------------------------+
| Milk (unknown % fat)                   | 297 mg                      |
+----------------------------------------+-----------------------------+
| Evaporated Milk                        | 164 mg                      |
+----------------------------------------+-----------------------------+
| Powdered Milk                          | 210 mg                      |
+----------------------------------------+-----------------------------+
| Coffee + Cream                         | 22 mg                       |
+----------------------------------------+-----------------------------+
| Yogurt                                 | 447 mg                      |
+----------------------------------------+-----------------------------+
| Ice Cream (unknown % fat)              | 84 mg                       |
+----------------------------------------+-----------------------------+
| Cooked Greens                          | 131 mg                      |
+----------------------------------------+-----------------------------+
| Broccoli                               | 36 mg                       |
+----------------------------------------+-----------------------------+
| Cold Cereal                            | 26 mg                       |
+----------------------------------------+-----------------------------+
| Hot Cereal                             | 24 mg                       |
+----------------------------------------+-----------------------------+
| 1 egg                                  | 24 mg                       |
+----------------------------------------+-----------------------------+
| Ca fortified juice                     | 289 mg                      |
+----------------------------------------+-----------------------------+
| Sardines                               | 351 mg                      |
+----------------------------------------+-----------------------------+
| Canned salmon                          | 60 mg                       |
+----------------------------------------+-----------------------------+
| Fish head Soup                         | 168 mg                      |
+----------------------------------------+-----------------------------+
| Kelp w/ herring eggs                   | 161 mg                      |
+----------------------------------------+-----------------------------+
| Small Black Fish                       | 236 mg                      |
+----------------------------------------+-----------------------------+
| Dried Salmon                           | 84 mg                       |
+----------------------------------------+-----------------------------+
| Whole Needlefish                       | 93 mg                       |
+----------------------------------------+-----------------------------+

^1^Calcium Values from Nobmann (1993) and using Nutrition Data System
Version 3.2 (Nutrition Coordinating Center of the University of
Minnesota, St. Paul, MN)