Chapter 1 – Introduction
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.
Figure 1-1 Normal Bone Density from Age 30 Years and Older
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Bone density measurements are interpreted in three ways, as g/cm2, 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.
<="502850833">Table 1-1 Selected Risk Factors for Osteoporosis According to the National Osteoporosis Foundation
Non-modifiable Risk Factors |
Potentially Modifiable Risk Factors |
| History of Fracture as an Adult | Current Cigarette Smoking |
| History of Fracture as an Adult in a First Degree Relative | Low Body Weight (<127 lb.) |
| 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
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I recruited subjects at a total of 17 Indian Health Service (I.H.S.) and tribal clinics in 12 municipalities (Figure 2-1). Clinics were selected as a convenience sample, rather than a random sample. I chose clinics using the following criteria: 1) approval of local tribal authority, 2) willingness of at least one health care provider to follow-up on patients at that clinic, 3) the availability of space at the clinic for subject interviews, and 4) the costs associated with the clinic visit were within the study budget. I attempted to maximize the geographical diversity of study sites as permitted by the site selection criteria. I recruited subjects among patients receiving care from the following clinics: four clinics at the Alaska Native Medical Center (ANMC) (Family Medicine, Diabetes, Internal Medicine, Orthopedic, and Women’s Health) and clinics at King Cove, Kodiak, Sand Point, Tatitlek, Nanwalek, Port Graham, St. Paul, Seward, Chenega Bay, Barrow, Bethel, and Kotzabue. I also recruited subjects among the Alaska Native and non-native staff of each clinic. Staff members were recruited either by e mail (ANMC) or by word of mouth.
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.
<="502850834"><="475694268">Table 2-1 Estimated Sample Size Given Different Assumptions of Prevalence and Bone Density Cut Point for Alaska Natives vs. Non-Natives| Criteria |
# Non-Native: #Native |
Native Prevalence | Non-Native Prevalence | Estimated # Native | Estimated #Non-Native |
Total Subjects |
| t-score <= - | 1:2 | 0.44 | 0.21 | 129 | 64 | 193 |
| t-score <= -2.5 low prevalence | 1:2 | 0.10 | 0.005 | 85 | 170 | 255 |
|
t-score <= -2.5 high prevalence |
1:2 | 0.10 | 0.05 | 368 | 735 | 1,103 |
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 ³ -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 R2 as formal tests of goodness of fit.
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 Supplements |
Continuous (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 Day 3x per Week |
Dichotomous |
|
Oral Corticosteroids for at Least Six Weeks at Any Time in Their Life |
Dichotomous |
|
Ever having Smoked Tobacco, Currently Smoking Tobacco, Not ever Smoking Tobacco |
Dichotomous |
|
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) |