In Turkey, households including at least one smoker spend nearly 8% of their monthly budget on tobacco, with a converse reduction in spending on food and utilities. While tobacco control policies (eg, increasing taxes on tobacco products and extending smoking bans) have decreased tobacco consumption, these policies have had limited impact on the spending patterns of smoking households.

BackgroundAnalysis of patient mobility in a country not only gives an idea of how the health-care system works, but also can be a guideline to determine the quality of health care and health disparity among regions. Even though determination of patient movement is important, it is not often realized that patient mobility could have a unique pattern beyond health-related endowments (e.g., facilities, medical staff). This study therefore addresses the following research question: Is there a way to identify regions with similar patterns using spatio-temporal distribution of patient mobility? The aim of the paper is to answer this question and improve a classification method that is useful for populous countries like Turkey that have many administrative areas.MethodsThe data used in the study consist of spatio-temporal information on patient mobility for the period between 2009 and 2013. Patient mobility patterns based on the number of patients attracted/escaping across 81 provinces of Turkey are illustrated graphically. The hierarchical clustering method is used to group provinces in terms of the mobility characteristics revealed by the patterns. Clustered groups of provinces are analyzed using non-parametric statistical tests to identify potential correlations between clustered groups and the selected basic health indicators.ResultsIneffective health-care delivery in certain regions of Turkey was determined through identifying patient mobility patterns. High escape values obtained for a large number of provinces suggest poor health-care accessibility. On the other hand, over the period of time studied, visualization of temporal mobility revealed a considerable decrease in the escape ratio for inadequately equipped provinces. Among four of twelve clusters created using the hierarchical clustering method, which include 64 of 81 Turkish provinces, there was a statistically significant relationship between the patterns and the selected basic health indicators of the clusters. The remaining eight clusters included 17 provinces and showed anomalies.ConclusionsThe most important contribution of this study is the development of a way to identify patient mobility patterns by analyzing patient movements across the clusters. These results are strong evidence that patient mobility patterns provide a useful tool for decisions concerning the distribution of health-care services and the provision of health care equipment to the provinces.

The aim of this study is to determine how the change in the balance between public-private sector employments affected public and private earnings during the 1990s and 2000s in Turkey. We use the Oaxaca-Blinder and quantile decomposition methods to determine the wage gap between public and private sectors utilizing the 1994 Household Income Distribution and Consumption Expenditure Survey and the 2008 Household Budget Survey conducted by the Turkish Statistical Institute. The study determined that the primary difference in the average wages between sectors arises from the differences in the endowments without correction for gender. After adjusting for correction using quantile regression, we find that the difference in the endowments between sectors at lower quantiles explains the majority of the raw wage gap; whereas a substantial amount of the raw wage gap is explained by the sector effect at higher quantiles.

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Detecting and explaining patient mobility patterns allows us to better understand linkages between socioeconomic facts. This research aims to reveal variables that affect the patient mobility among cities in Turkey. It considers not only the health-related factors but also socioeconomic, demographic, and geographic variables to analyze the patient mobility. The data covers 40 million external patient admission to health facilities between 2010 and 2013. The most common clinics (cardiology, pediatric, obstetric, and internal diseases) selected to focus on branch level differences. The random effects regression model was used due to the presence of time-invariant variables on the basis of gravity model. There are statistically significant positive relationships between migration and patient mobility for all the clinics studied. The distance between two provinces has a negative impact on patient movements. Statistically significant relationships in patient mobility are observed for all clinics when two provinces are contiguous. It is observed that patients are moving from the low-income provinces to those having higher income. As a result, apart from the health-related variables, socioeconomic, demographic and geographical factors also have a substantial effect on patient mobility. While generalizing the results, it should be kept in mind that a limited number of clinics are studied.