29 research outputs found
Poverty effects of public health reforms in Turkey: A focus on out‐of‐pocket payments
No full textRationale, aims, and objectives Expanded financial coverage is critical to fight against poverty through public health reforms in developing countries. This study explores inequity in public health financing reforms in Turkey between 2003 and 2015. Methods This paper has two parts. The first part examines inequity in health care financing in Turkey between 2003 and 2015. Gini, entropy (Theil and mean logarithmic deviation), and Atkinson indexes were calculated. In the second part of the paper, we investigated the degree of progressivity by using Kakwani index and Lorenz and concentration curves. Results We found a decreasing trend in terms of inequity. After major public health reforms and unification of the health financing system, it is seen that the distribution of out-of-pocket expenditure on health stands on the shoulders of vulnerable groups. Conclusions Study results provide a deep understanding of the effects of poverty on public health financing reforms on households in Turkey. To reduce out-of-pocket health spending inequities and to protect vulnerable groups from increasing the level of health expenditures, we suggest that the government enlarges health insurance coverage for the poor
Integrated k-means clustering with data envelopment analysis of public hospital efficiency
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Comparison of Fuzzy C-Means and K-Means Clustering Performance: An Application on Household Budget Survey Data
No full textNonclinical predictors of caesarean section: a path analytic approach
No full textThe objective of this study was to explore nonclinical predictors of cesarean sections (CS) and how they interact with each other, specifically in Turkey. Data was gathered from official statistical records for the year 2017 from the 81 different provinces throughout Turkey. A path analytic model was constructed to examine the interrelationships between socioeconomic factors, utilization of health services, patient satisfaction, and number of CS procedures. The overall performance of the final path model was quite good (GFI = 0.98, AGFI = 0.93, and CFI = 0.96). The study results emphasize the substantial impact of an increase in the number of hospital admissions on the increase in the rate of CS procedures (PC = 0.70). Additionally, the increase in the number of hospital admissions mediates the interrelationship between geographic region, high education, and CS. The findings demonstrate the significant interrelationships among the several major nonclinical predictors of CS in Turkey.Impact Statement What is already known on this subject? There has been a considerable increase in the rate of CS in Turkey and the current study examined the nonclinical predictors of CS, and how they interact with each other, specifically in Turkey. The insights developed by this study are due to its scope and topicality. Although of course clinical factors associated with CS are reflected in the literature, this study focused on nonclinical predictors of CS. What the results of this study add? This study empirically clarifies the causal interrelationships among nonclinical predictors of CS, using data from Turkey where CS rates are very high, causing great concern by health professionals and decision-makers. The results of this study provide a stronger understanding of how nonclinical factors relate to CS in Turkey. Significant factors include the connective role of geographic region, the increasingly high level of education being received by women, and the total number of hospital admissions. What the implications are of these findings for clinical practice and/or further research? Study results empirically prove interconnection among geographic region, education, health services utilisation and the number of CS. Health decision makers need to consider the important indirect effects of region, education and number of hospital admissions on the demand for CS procedures
Evaluating Physician and Manager Perspectives on EHR Usage in Turkey: A Factor Analysis Approach
No full textAims and objectives: Understanding the perspectives of health professionals about Electronic Health Records (EHRs) is pivotal for better management of health information systems (HIS). The purpose of this study is to examine explanatory factors of usage of EHRs according to the physician and hospital manager's evaluations. Methods: A survey was administered in three hospitals in the & Idot;zmir metropolitan area, and 202 physicians and hospital administrators participated in this study. The internal consistency of the questionnaire was assessed using Cronbach's Alpha (0.74), and the suitability of the factor analytical model was assessed using KMO (0.79) and Bartlett's test (X-2 = 1720.97, p < 0.001). Exploratory factor analysis was performed with Varimax rotation to determine the factors underlying the model. Then, confirmatory factor analysis (CFA) was performed to reveal the latent structure of the model. Results: The performance of CFA model is statistically significant (p < 0.0001), acceptable at moderate level (X-2/df = 3,81) the goodness-of-fit indices are good (CFI = 0,87; GFI = 0,76; NFI = 0,83; AGFI = 0,70). Three factors explain the latent structure of this model and evaluations of physician and hospital managers towards the usage of EHRs named as: benefits of usage of EHRs; concerns about the usage of EHRs and the effect of EHRs on the quality of work, efficiency, access to the information and safety. Conclusion: Study results highlight the necessity of comprehending the EHR from the perspectives of health professionals and managers by focusing on the advantages of EHRs, concerns towards the deployment of EHR systems, and the improvement effects of EHR in work quality, efficiency, and HIS
OP134 Predictors Of Public Health Outcomes: A Case Study From Turkey
No full textINTRODUCTION:In Turkey, there is a scarcity of knowledge about the predictors of health outcomes at a national level, and it is well known that there is a gap between rural and urban parts of developing countries in terms of the level of health outcomes. This study aims to find out predictor factors of the public health outcomes at a province level in Turkey.METHODS:Life expectancy at birth and mortality are used as public health outcome indicators. Logistic regression and Random Forest classification generated by using 50, 100, and 150 trees were used to compare prediction performance of health outcomes. The results of different prediction methods were recorded changing the “k” parameter from 3 to 20 in k-fold cross validation. The Area Under the ROC Curve (AUC) was used as a measure of prediction accuracy. Prediction performance differences were tested using Kruskall-Wallis analysis and visualized on a heatmap. Finally, predictor variables of public health outcomes were shown on a decision tree.RESULTS:Study results revealed that Logistic regression outperformed Random Forest classification. The difference between all prediction methods to predict public health outcome indicators was statistically significant (p<.000). The heatmap shows that AUC values to predict mortality have superior performance when compared with life expectancy at birth. Decision tree graphs present that the most important predictor variables were total number of beds for mortality and percentage of higher education graduates for life expectancy at birth.CONCLUSIONS:The results of this study represent a preliminary attempt to determine public health outcome indicators. It is hoped that the results of this study serve as a basis to understand the determinants of health care outcomes at province level with focus on a developing country. This study illustrates that there is a need to spend extra effort for future studies to analyze public health outcomes to improve social welfare functions in health systems.</jats:sec
Demographic and Welfare State Predictors of Out-of-Pocket Health Expenditures: A Path Analytic Model
No full textOut-of-pocket (OOP) payments are principal components of financing healthcare and have a significant effect on poverty in numerous developing countries. The present study seeks to ascertain the relation among demographic, welfare state, and OOP health expenditure indicators using a path analysis. National representative household budget data from the Turkish Statistical Institute for 2015 were used. To test the goodness of fit of the model, multiple fit indices were utilized. The model fit for redefined path analytic model data was good (X-2/df = 70.20/9 = 7.8; RMSEA = 0.032; GFI = 1.00; AGFI = 0.99; CFI = 0.99). The results of the analysis revealed that demographic and welfare indicators are causally related to OOP health expenditures, and income was a mediating factor for this interrelationship. Designing of socially inclusive policies on the basis of the values of equity is essential to combat poverty due to OOP health expenditures in developing countries
