The interaction of ethnicity and chronic disease as risk factors for osteoporotic fractures: a comparison in Canadian Aboriginals and non-Aboriginals
- First Online:
- 156 Downloads
Efforts to develop global methods for absolute fracture risk prediction are currently limited by uncertainty over the validity of these models in non-White populations. Aboriginal Canadians have higher fractures rates than non-Aboriginals. This analysis examined the interaction of ethnicity with diabetes mellitus, disease comorbidity and substance abuse as possible explanatory variables.
A retrospective, population-based matched cohort study of fracture rates was performed using Manitoba administrative health data (1984–2003). The study cohort consisted of 27,952 registered Aboriginal adults (aged 20 years or older) and 83,856 non-Aboriginal controls (matched three to one for year of birth and gender). Diabetes mellitus, number of ambulatory disease groups (ADGs), substance abuse and incident fractures were based upon validated definitions. Poisson regression analyses of fracture rates modelled the explanatory variables as main effects and two-way interactions with ethnicity.
Osteoporotic fracture rates were approximately twofold higher in the Aboriginal cohort (p<0.0001). Diabetes, greater number of ADGs and substance abuse were all more common in the Aboriginal cohort (all p<0.0001). These factors were associated with increased fracture rates (all p<0.0001) and significantly higher population attributable risk percent in the Aboriginal cohort (all p<0.0001). However, no significant interactions between the risk factors and ethnicity were observed (p>0.1 for all interaction effects).
Greater prevalence of diabetes, comorbidity and substance abuse contributes to higher rates of fracture. The relative risk of fracture for these factors is similar for both Aboriginal and non-Aboriginals despite large differences in absolute fracture risk and risk factor prevalence.
KeywordsDiabetes mellitus Ethnicity Fractures Indians, North American Morbidity Osteoporosis Substance abuse
- 5.Lunt M, Felsenberg D, Reeve J, Benevolenskaya L, Cannata J, Dequeker J, Dodenhof C, Falch JA, Masaryk P, Pols HA, Poor G, Reid DM, Scheidt-Nave C, Weber K, Varlow J, Kanis JA, O’Neill TW, Silman AJ (1997) Bone density variation and its effects on risk of vertebral deformity in men and women studied in thirteen European centers: the EVOS Study. J Bone Miner Res 12(11):1883–1894PubMedCrossRefGoogle Scholar
- 14.Martens P, Sanderson D, Bond R, Jebamani L, Burchill C, Roos N, Tanner-Spence M, Derksen S, Leader A, Beaulieu M, Elias B, Steinbach C, O’Neil J, MacWilliam L, Walld R, Dik N (2002) The health and health care use of registered First Nations people living in Manitoba: a population-based study. Manitoba Centre for Health Policy, Winnipeg, ManitobaGoogle Scholar
- 15.Income quintiles based on the 1996 census. Manitoba Centre for Health Policy . 2003. URL:http://www.umanitoba.ca/academic/centres/mchp/concept/diet/income/income_quintile.html (Last accessed June 6, 2005)
- 22.Reed M, Demeter S, Lix L, MacWilliam L (2004) Is there an association between socioeconomic factors and the per capita utilization of selected diagnostic imaging modalities? In: Finlayson G, Leslie WD, MacWilliam L (eds) Diagnostic imaging data in Manitoba-assessment and applications. Manitoba Centre for Health PolicyGoogle Scholar
- 24.Martens P, Burchill C, Freund J, DeCoster C, McKeen N, Ekuma O, Prior H, Chateau D, Burland E, Robinson R, Jebamani L, Metge C (2004) Patterns of regional mental illness disorder diagnoses and service use in Manitoba: a population-based study. Manitoba Centre for Health Policy, Winnipeg, ManitobaGoogle Scholar
- 25.McCulloch CE, Searle SR (2001) Generalized, linear, and mixed models. Wiley, New YorkGoogle Scholar
- 26.Fox J (1997) Applied regression analysis, linear models, and related methods. Sage, Thousand Oaks, CAGoogle Scholar
- 31.Ross PD, He Y, Yates AJ, Coupland C, Ravn P, McClung M, Thompson D, Wasnich RD (1996) Body size accounts for most differences in bone density between Asian and Caucasian women. The EPIC (Early Postmenopausal Interventional Cohort) Study Group. Calcif Tissue Int 59(5):339–343PubMedCrossRefGoogle Scholar
- 37.Cooper R (2002) A note on the biological concept of race and its application in epidemiologic research. In: LaVeist TA (ed) Race, ethnicity and health: a public health reader 1st(6). Jossey-Bass, San Francisco, pp 99–114Google Scholar
- 38.Muntaner C, Nieto FJ, O’Campo P (2002) The bell curve: on race, social class, and epidemiologic research. In: LaVeist TA (ed) Race, ethnicity and health: a public health reader 1st(8). Jossey-Bass, San Francisco, pp 129–140Google Scholar
- 39.Leslie WD, Metge CJ, Weiler HA, Yuen CK, Krahn J, Doupe M, Salamon EA, Wood Steiman P, O’Neil J, Greenberg CR, Lix L, Roos LL, Tenenhouse A (2004) Site specific reductions in bone density in Canadian Aboriginal women: the First Nations Bone Health Study. J Bone Miner Res 19(Suppl 1):S288Google Scholar
- 44.Campbell M, Diamant RMF, MacPherson BD, Grunau M, Halladay J (1994) Energy and nutrient intakes of men (56–74 years) and women (16–74 years) in three northern Manitoba Cree communities. J Can Diet Assoc 55:167–174Google Scholar