2015 | Dec
Cardiovascular diseases (CD) have been the main cause of death in Greece and the second cause of death worldwide during the last few years. According to the Hellenic Statistical Authority, in 2012, 42.6% of deaths (i.e. 49,728 people) in Greece were caused by CD. According to a report by the World Health Organization (Mendis et al. 2011), in 2008, deaths caused by CD represented 31% or 17.3 million deaths worldwide, with great inequalities between developing and developed countries. In our country, the prevalence of CD – i.e. its frequency in the general population and the risk factors for CD, were investigated by several studies: the Seven Countries Study, Athens Heart, EPIC-Greek and ATTICA; which showed that Greece’s population has shifted from low to high risk (Panagiotakos et al. 2008).
This paper attempts to highlight the spatial inequalities in health status with regard to the risk of cardiovascular diseases in a sample of Attica’s population, by means of mapping and by calculating relevant indicators. The results presented below come from an exploratory spatial analysis of healthcare levels in the population sample and they cannot be generalised to the whole population of Attica because the sample is not necessarily representative. However, we try to highlight the importance of studying the spatial aspect of the phenomenon so that CDs can be linked to environmental factors that may influence their unequal spatial distribution in the future.
The population sample under examination was selected as part of the THISEAS Study using data of patients with coronary heart disease (CHD) in cardiology clinics of hospitals in Attica. These were outpatients or inpatients without a medical history of CD. Moreover, we also collected data of people with no medical history of CD from Municipalities and Seniors Open Protection Centres (KAPI) in Kallithea, Moschato and Nea Smyrni. The data collected included cardiovascular risk factors. For the purposes of this work, a digital database of geospatial data was created with reference to the geographical location (residence) of respondents and with the help of an open source Geographic Information System.
The THISEAS study
The THISEAS study (The Hellenic study of Interactions between Snps and Eating in Atherosclerosis Susceptibility) is a case-control study which aims to (a) highlight new gene polymorphisms affecting the risk of coronary heart disease (CHD), (b) identify dietary patterns modifying the risk of CHD and c) check the interaction between dietary patterns and genetic composition regarding CHD risk.
CHD has a multifactorial aetiology, which includes both genetic and environmental factors, such as diet. Identifying CHD risk factors has been one of the most significant achievements of epidemiological investigations. Risk factors are decisive in assessing a person’s risk of suffering from the disease. It is important that these factors be evaluated, as they can form the foundation for prevention or treatment. The most common way of classifying risk factors is by dividing them into modifiable and non-modifiable. Modifiable factors include arterial hypertension, diabetes, dyslipidaemia, obesity, smoking and lack of physical activity. Non-modifiable factors include gender, age, family history of premature coronary heart disease (Jorde et al. 2003) and genetic factors (Chaer et al. 2004). Identifying modifiable factors such as physical exercise and diet helps a lot in preventing and reducing the probability of CHD.
The spatial analysis of health data is a relatively new research area for quantitative geography and for health sciences that could broadly be defined as spatial epidemiology. In this subject, we can distinguish two major categories of spatial analysis techniques: spatial point techniques and regional analysis of grouped disease cases.
In this paper, the dots refer to individuals who either do not suffer from CHD (controls) or patients with CHD, as shown in Map 1. Both the map and the calculation of the K-function (Ripley 1977) show that patients with CHD are not randomly distributed in space but are spatially concentrated. Given these spatial clusters, it is very interesting to examine whether specific health characteristics of the respondents have spatial similarities or differences. Spatial autocorrelation is a method we can use to determine whether observations of a variable that are spatially close to each other also have similar or dissimilar values. The spatial Gini inequality coefficient combined with the coefficient of Moran’s I spatial autocorrelation (Καλογήρου 2011) help us detect spatial inequalities in health data and possible spatial clusters of respondents with similar health data for the population sample under examination.
The Gini coefficient is widely used in economics for measuring income inequality. It assumes values from 0 to 1, where 0 indicates complete income equality while 1 complete income inequality. The traditional Gini coefficient is calculated based on individual income, regardless of the geographical location of each person. According to Rey and Smith (2013), the spatial separation of the traditional Gini coefficient allows us to calculate inequality between neighbouring and non-neighbouring observations. These coefficients were calculated using Ictools (Kalogirou 2015).
The results of the exploratory spatial analysis
Out of the usual factors associated with the occurrence of CHD, this paper analyses Body Mass Index (BMI), arterial hypertension, age and gender. The analysis includes descriptive statistics and spatial coefficients, presented in Table 1. The frequencies shown in the second column of the table were calculated after the cases for which no data existed were removed. The Gini and Moran’s I coefficients were calculated with weights wij=1 for the 12 closest neighbours and 0 for the rest. The coefficient values in bold are statistically significant at a confidence level greater than 95%.
Graph 1a. Descriptive statistics of the population sample examined for coronary artery disease and the factors associated with it
Graph 1b. Spatial indexes of the population sample examined for coronary artery disease and the factors associated with it
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