Academic journal article Iranian Journal of Public Health

Socioeconomic Inequalities in Non-Communicable Diseases and Self Assessed Health in Turkey

Academic journal article Iranian Journal of Public Health

Socioeconomic Inequalities in Non-Communicable Diseases and Self Assessed Health in Turkey

Article excerpt


Burden of non-communicable diseases (NCDs) are increasing worldwide and global disease bur-den is shifting away from communicable to non-communicable diseases in terms of disability ad-justed life years for the last two decades (1). In 2008, of the 57 million global deaths, 63% percent were due to NCDs and 80% of all these deaths occurred in low and middle income countries where most of the world's population also live there (2). Turkey is experiencing an epidemi-ologi-cal transition like many developing countries and burden of NCDs and its risk factors are increasing in recent years. As a result, Ministry of Health started to pay more attention to reduce the burden of NCDs and prepared action plans to tackle with them (3). Hence, the surveillance of NCDs, their determinants and collecting data on other health related measures became an important task for Turkey. Self-reported morb-idity and self-assessed health are measures that are widely used in epidemiological studies which are closely related with morbidity, mortality, health services utiliza-tion and socioeconomic status (4, 5) Even there is a universal health insurance and there are some improvements in access to health care during last decade, some disparities in health outcomes still exist between geographical regions, education sta-tus and income groups in terms of infant mortality and self-assessed health in Turkey (6, 7). Reducing inequity in health outcomes is a major challenge in every country. Studies that evaluate the socioeco-nomic determinants of health may inform policy makers on effective measures to reduce inequities in health outcomes (8). Even there are some re-ports and studies on burden of NCDs and poor SAH in Turkey; few have assessed the socioeco-nomic determinants of these conditions (9).

Recent health economics literature from various settings provided tools for quantifying contribu-tions of determinants to observed inequalities (10, 11). Being able to differentiate and grade the deter-minants of health inequalities enables prio-ritizing and targeting equitable health interventions. A re-cent study decomposed inequalities in SAH in Tur-key for year 2003 however there is a lack of knowledge in composition of socioeco-nomic ine-qualities in chronic diseases using inequ-ality measures in our setting (12).

The aim of this study was to use a concentration index (CI) to quantify the socioeconomic distribu-tion of not only SAH but also self-reported chronic conditions and to decompose these inequalities by quantifying the contributions of potential determi-nants such as age, gender, wealth, education level, marital status and geographical area lived in in Tur-key for the first time using data from the Turkish Health Survey 2008.


Source of data and study design

Cross-sectional data from the national Turkish Health Survey conducted by Turkish Statistical Institute (TURKSTAT) during year 2008, covering 14,655 adults aged 15 or older were analyzed. All settlements in the territory of Republic of Turkey were covered in sample selection. Sample size of the survey is calculated to do estimations on the base of total of Turkey, urban and rural. Thus, the total sample size necessary was found to be 7 910 households. Since non-response is also taken into account when calculating the sample size, substitu-tion for household or individual were not used in the survey. The sampling method of the survey was two staged stratified cluster sampling method. Clusters were selected in the first stage, households were selected within each selected clusters in the second stage. For urban areas and rural areas with municipal organization clustering was applied where a cluster included approximately 100 ad-dresses. In the final stage, households were selected systematically within each selected cluster. These were selected with equal probability using system-atic sampling method. The sampling frame used for this survey was the National Address Based Population Registry System (dated March 2008). …

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