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Access and Use of Health Services by People with Diabetes from the Item Response Theory.

Isabela Silva Levindo de SiqueiraRafael Alves GuimarãesValéria PagottoClaci Fátima Weirich RossoSandro Rogério Rodrigues BatistaMaria Alves Barbosa
Published in: International journal of environmental research and public health (2022)
The objective of this study was to analyze the indicators of access and use of health services in people with diabetes mellitus. This study used data from the National Health Survey, conducted in Brazil in 2013. The National Health Survey was carried out with adults aged 18 years or older residing in permanent private households in Brazil. Indicators from 492 individuals with self-reported diabetes mellitus living in the Central-West region of the country were analyzed. Item response theory was used to estimate the score for access to and use of health services. Multiple linear regression was used to analyze factors associated with scores of access and use of health services by people with diabetes mellitus. The mean score of access estimated by the item response theory and use estimated was 51.4, with the lowest score of zero (lowest access and use) and the highest 100 (highest access and use). Among the indicators analyzed, 74.6% reported having received medical care in the last 12 months and 46.4% reported that the last visit occurred in primary care. Only 18.9% had their feet examined and 29.3% underwent eye examinations. Individuals of mixed-race/skin color and those residing outside capital and metropolitan regions had lower access and use scores when compared to white individuals and residents of state capitals, respectively. The study shows several gaps in the indicators of access and use of health services by people with diabetes. People of mixed race/skin color and residents outside the capitals and metropolitan regions had lower scores for access and use, suggesting the need to increase health care in these groups.
Keyphrases
  • primary care
  • healthcare
  • type diabetes
  • cardiovascular disease
  • glycemic control
  • metabolic syndrome
  • skeletal muscle
  • machine learning
  • health insurance
  • electronic health record
  • deep learning