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Tytuł pozycji:

Comparative analysis of socioeconomic, behavioral and biological factors between healthy patients and patients with newly diagnosed diabetes in the Lubuskie Voivodeship

Tytuł:
Comparative analysis of socioeconomic, behavioral and biological factors between healthy patients and patients with newly diagnosed diabetes in the Lubuskie Voivodeship
Autorzy:
Bonikowska, I.
Jasik-Pyzdrowska, J.
Szwamel, K.
Powiązania:
https://bibliotekanauki.pl/articles/2087637.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Opolski. Instytut Nauk o Zdrowiu
Tematy:
patients
type 2 diabetes mellitus
prediabetic state
Źródło:
Medical Science Pulse; 2020, 14, 3; 55-63
2544-1558
2544-1620
Język:
angielski
Prawa:
CC BY-NC-SA: Creative Commons Uznanie autorstwa - Użycie niekomercyjne - Na tych samych warunkach 4.0
Dostawca treści:
Biblioteka Nauki
Artykuł
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Background: The most effective way to prevent an increasing diabetic population lies in early detection of risk factors and diagnosis of carbohydrate metabolism disorders. Aim of the study: The study aimed at determining socio-economic variables, lifestyle behaviours and biological factors differentiating patients with newly diagnosed diabetes from diabetes-free individuals. Material and methods: Assessment of diabetic vs. non-diabetic individuals was performed according to the American criteria issued by the Commission on Social Determinants of Health as well as the FINDRISC form, which helps identify patients who are at risk of developing type 2 diabetes on the basis of multi-factorial determinants of its development. The research was conducted in 2018 among 1167 primary health care patients from Lubuskie Voivodeship using a diagnostic survey method which interviewed the respondents according to the FINDRISC standard questionnaire. Results: The group of healthy patients was similar to the group of patients with newly diagnosed diabetes with respect to variables such as age (p=0.713), sex (p=1), place of residence (p=1), level of education (p=0.076), professional activity (p=0.758), BMI (p=0.133), waist measurement (p=0.665), frequency of fruit and vegetables intake (p=0.572), frequency of taking hypotensive medications (p=0.176), frequency of diabetes occurrence in the family history (p=0.227) and physical activity (p=0.321). Conclusions: Early detection of carbohydrate metabolism disorders, with the use of standardised tools that assess diabetes development, appears to be essential in the prevention of this disorder. Therefore, there is a strong need to create a tool adjusted to socio-demographic factors such as geographical location, economic conditions and lifestyle. Additionally, active and massive screening for carbohydrate metabolism disorders in patients with a low risk of diabetes seems to be crucial in its prevention.

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