Unmet Medical Care Needs Due To Payment Difficulty release_7d4xiuz32bdcnk5amvic4qup24

Abstract

<jats:title>Abstract</jats:title> <jats:bold>Objective:</jats:bold> This study aims to determine the effects of chronic diseases and socio-economic factors on payment difficulty in medical care.<jats:bold>Methods: </jats:bold>The variables used in the analysis were obtained from the "2016 TURKSTAT Health Survey" micro data set. Three models were established to determine the degree of chronic disease data and socio-economic variables affecting the payment difficulty in medical care. Binary Logit Regression analysis was used to analyze the models.<jats:bold>Findings:</jats:bold> In terms of payment difficulty in medical care; age, education, household income, social security institution (SGK) treatment cost, general health insurance (GSS) treatment cost, other treatment cost, reason for not working, work continuity, working method, overall health status, being sick longer than 6 months, vital activity restriction, asthma, bronchitis, coronary heart failure, arthrosis, waist and neck disorders, allergy, liver failure, kidney disease, depression, other chronic diseases, wearing glasses, physical pain state, pain preventing life, feeling worthless, receiving bed service for the last 12 months, receiving daily service for the last 12 months, drug use by his own decision, cholesterol measurement status, blood glucose measurement status, stool occult blood test measurement status, being late for appointment, payment difficulty in dental care, in drug and in spiritual treatment, tobacco use status and exposure to tobacco smoke were effective (p&lt;0,05). According to Cohen d, the groups with the strongest effect on payment difficulty in medical care are payment difficulty in dental care, drug and spiritual treatment, delay due to transportation, depression and chronic bronchitis (E.B. &gt; 0,8).<jats:bold>Conclusions: </jats:bold>According to the results of the research, it was determined that chronic diseases and socio-economic variables are effective in the payment difficulty of medical care. Policymakers can benefit from evidence-based on econometric models of the comparative burden of different chronic conditions, demographic and economic structure.
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