Literature DB >> 21039080

Rural-urban disparities in the management and health issues of chronic diseases in Quebec (Canada) in the early 2000s.

Alain Vanasse1, Josiane Courteau, Alan A Cohen, Maria Gabriela Orzanco, Catherine Drouin.   

Abstract

INTRODUCTION: The 'Commission on the Future of Health Care in Canada' recognized that people living in rural and remote areas of Canada are at a disadvantage in health status, access to care and health professionals, and it considers the fight against these problems as a national priority. Although some attention has been paid to the prevalence of chronic diseases, very few studies have studied specifically the management and health issues in populations with chronic diseases in relation to rurality. The objective of this study was to describe systematic gaps across rural and urban populations in incidence, mortality, morbidity, material and human resources utilization, and drug management for three important chronic diseases: atherosclerosis, osteoporosis and diabetes.
METHODS: Three retrospective population-based cohort studies were used. Three study populations were selected: an atherosclerotic population including patients newly hospitalized for a myocardial infarction (MI), an osteoporotic population including the at risk population who have suffered from a fragility fracture (FF) and, finally, a diabetic population that includes only incident cases of diabetes patients. For each of the three chronic diseases, variables were selected and classified in six categories: incidence, mortality, morbidity, material resources utilization, physician consultation and drug treatment. The Statistical Area Classification (SAC) was used as the rurality definition and contains six categories including two urban areas--Census Metropolitan Areas (CMA), or metropolitan areas, and Census Agglomeration (CA), or small towns--and four rural areas: Strong, Moderate, Weak and No Metropolitan influenced zones (MIZ), depending on the proportion of the workforce that commutes to urban areas. Each disease-related variable was described using age- and sex-adjusted rates. For comparing rates between rurality classes, the adjusted relative risks were calculated using the CMA as the reference group. The X2 was used to test for the equality of risks.
RESULTS: A common pattern was identified from this study: for all three studied diseases, the material resources utilization rates and the specialist (other than internist) consultation rates were almost always statistically lower in small towns and rural areas when compared with metropolitan areas. Mortality rates and drug utilization rates were very similar among regions, except for hormone replacement therapy in women where utilization rates were higher in small towns and rural areas compared with metropolitan areas. Among observations that were not common to all three chronic diseases, the first is that MI incidence was greater in small towns and in Weak MIZ compared with metropolitan areas, fragility fractures seem to be marginally more frequent in small towns but less frequent in rural areas compared with metropolitan areas, while an increased incidence rate of diabetes is observed in remote region and a smaller risk in moderate MIZ compared with metropolitan areas. For both atherosclerosis and diabetes, morbidity rates were always statistically higher in small towns and in rural areas. This was not the case for patients with osteoporotic fractures where similar morbidity rates across regions were observed, except in strong MI which show the lowest morbidity rate.
CONCLUSIONS: There was substantially lower utilization of specialized services in non-metropolitan areas for all three diseases (myocardial infarction, osteoporosis, and diabetes). However, this did not translate into consistent differences in mortality and morbidity outcomes. This suggests that the impact of differential care utilization is specific to each disease, with indications that some important services may be under-utilized in rural areas, while others may be over-utilized in urban areas without improvement in outcomes.

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Mesh:

Year:  2010        PMID: 21039080

Source DB:  PubMed          Journal:  Rural Remote Health        ISSN: 1445-6354            Impact factor:   1.759


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