An ecological study on the geographic patterns of ischaemic heart disease in Portugal and its association with demography, economic factors and health resources distribution

OBJECTIVES: Being one of the main causes of morbidity and mortality in developed countries, ischaemic heart disease's (IHD) incidence and mortality present clear differences between and within countries. Several authors already proposed possible explanations based on the demography, environmental factors, diet and level of urbanisation. This study reflects the Portuguese reality concerning IHD, by analysing the geographical distribution of hospital admissions and mortality due to this condition, in Portugal, and its association with demography, economical factors and the distribution of healthcare resources at the regional level. DESIGN: Ecological study. SETTING: Data from all Portuguese Public Hospitals were obtained using the National Registry of Hospital Admissions, between 2000 and 2007, and data on demography, economical factors and health resources distribution were obtained from the National Institute of Statistics. PARTICIPANTS: Aggregated statistics on hospital admissions and mortality were computed for 278 counties based on almost 200 000 admissions. PRIMARY AND SECONDARY OUTCOME MEASURES: Mortality rate; hospital admissions rate. RESULTS: The geographical distribution of non-adjusted mortality and hospital admission showed an inner/coastal pattern but no North/South gradient was clear. Counties with higher economical development had significantly higher mortality and admission rates. However, healthcare resources distribution was not significantly associated with IHD hospital admission and mortality. When adjusted for age, gender, economic development and health resources distribution, there was still unexplained geographical variation both in hospital admissions and mortality rates. CONCLUSION: A pattern in the geographic distribution of incidence and mortality of IHD was clear even after the adjustment for age and gender. Economical variables were the ones presenting the strongest association. These types of analysis may be very helpful for the definition of health policies, in particular to identify priority regions for disease prevention and guidelines for healthcare resources distribution.