TY - JOUR
T1 - Determinantes de la diabetes en Perú
T2 - contexto COVID-19
AU - Meléndez, Lindon Vela
AU - Caicedo, Yefferson Llonto
AU - Carrasco-Bonilla, Anyela Marilu
AU - Zuloeta, Jaime Ysrael Salazar
AU - Ramos, Jorge Guillermo Morales
AU - Garcés, Elena Miriam Chávez
AU - Navarro, Enaidy Reynosa
N1 - Publisher Copyright:
© 2023, Editorial Ciencias Medicas. All rights reserved.
PY - 2023/1/27
Y1 - 2023/1/27
N2 - Objective: To determine the determining factors of diabetes in Peru in the context of COVID-19 based on data processing from the Demographic and Family Health Survey 2020 of module 1640 (Health)-CSALUD01 and module 1630 (Characteristics of housing)-RECH23 published by the National Institute of Statistics and Informatics (INEI). Methodology: Quantitative approach, with a type of explanatory research. The data was obtained from the 2020 Demographic and Family Health Survey data files of module 1640 (Health)-CSALUD01 and module 1630 (Characteristics of housing)-RECH23 published on the INEI web portal: Results: The binary logistic regression model shows in the assertiveness matrix that it better predicts Yes (100%) than No (93.7%), being the elaborated model the one that has correctly classified 96.3% of the cases. The increase in the relative probability of having diabetes in the context of COVID-19 is mainly associated with the diagnosis of arterial hypertension (2.36), age (0.57), obesity (0.46), poverty (0.20); while the relative probability that the condition of suffering diabetes decreases is to have health insurance (-0.49), consumption of fruits (-0.45) and vegetables (-0.34). Conclusions: The statistically significant factors that explain the probability of being diagnosed with diabetes mellitus are economic factors (poverty), health factors (obesity, arterial hypertension, depression, tiredness, sleep), cultural factors (consumption of fruits and vegetables), and variables social (age).
AB - Objective: To determine the determining factors of diabetes in Peru in the context of COVID-19 based on data processing from the Demographic and Family Health Survey 2020 of module 1640 (Health)-CSALUD01 and module 1630 (Characteristics of housing)-RECH23 published by the National Institute of Statistics and Informatics (INEI). Methodology: Quantitative approach, with a type of explanatory research. The data was obtained from the 2020 Demographic and Family Health Survey data files of module 1640 (Health)-CSALUD01 and module 1630 (Characteristics of housing)-RECH23 published on the INEI web portal: Results: The binary logistic regression model shows in the assertiveness matrix that it better predicts Yes (100%) than No (93.7%), being the elaborated model the one that has correctly classified 96.3% of the cases. The increase in the relative probability of having diabetes in the context of COVID-19 is mainly associated with the diagnosis of arterial hypertension (2.36), age (0.57), obesity (0.46), poverty (0.20); while the relative probability that the condition of suffering diabetes decreases is to have health insurance (-0.49), consumption of fruits (-0.45) and vegetables (-0.34). Conclusions: The statistically significant factors that explain the probability of being diagnosed with diabetes mellitus are economic factors (poverty), health factors (obesity, arterial hypertension, depression, tiredness, sleep), cultural factors (consumption of fruits and vegetables), and variables social (age).
KW - COVID-19
KW - Diabetes mellitus
KW - Risk factors
UR - http://www.scopus.com/inward/record.url?scp=85147576652&partnerID=8YFLogxK
M3 - Artículo
AN - SCOPUS:85147576652
SN - 0864-0300
VL - 42
JO - Revista Cubana de Investigaciones Biomedicas
JF - Revista Cubana de Investigaciones Biomedicas
M1 - e2711
ER -
