RESUMEN
BACKGROUND: Low health literacy (HL) is associated with an extensive range of health outcomes. OBJECTIVE: The present study was performed to inquire about the relationship between HL and glycemic control in gestational diabetes in order to design interventional future preventing programs. METHODS: This cross-sectional study was performed on 104 Iranian pregnant women with gestational diabetes mellitus (GDM) referred from urban and rural areas to endocrinology clinic of Hamadan Beheshti Hospital, in 2017. Iranian Health Literacy Questionnaire (IHLQ) and a sociodemographic checklist were distributed among women. Correlation between HL and glycemic control was examined using SPSS. The significance level was set at P < 0.05. RESULTS: Among women, 48.1% (50) were affected by uncontrolled diabetes and only 22% (11) had an adequate level of HL. An adequate level of HL were 50% and 22% in glycemic controlled and uncontrolled women, respectively. In univariate analysis, there was a significant relationship between diabetes control and adequate HL. So, problematic HL could increase the chance of uncontrolled diabetes more than three times (odds ratio: 3.5; CI: 1.5-8.3; P value: 0.004). Among all related variables, education and being housewife were considered as protective and risk factors for problematic HL, respectively. CONCLUSION: In conclusion, this study has provided evidence of limited HL and its relationship with low glycemic control in pregnant women with GDM. The problem was more serious in low educated, rural, housekeepers, and older-aged women. This deficit needs to be addressed by health planners and policymakers who are responsible for promoting the health of people and decreasing health inequalities community.
RESUMEN
Data acquired from aerial laser scanner systems are increasingly used for detecting individual trees in operational inventories. In conventional analyses, tree detection is often performed on raster models that use local height maxima filters; an option that is likely to accumulate important errors. In order to reduce errors and improve the detection of individual trees, a new method is proposed that uses an Absolute Height Maxima (AHM) filter applied on the original point clouds obtained from Aerial Laser Scanning (ALS). ALS point clouds at a density of 2 to 4 points per square meter were acquired over forest stands in Hyrcanian forests. In the new method, false trees and commission errors were automatically found and excluded. To evaluate the efficiency of this new method, 121 sample trees in the field were located, with a DGPS and a mapping camera. The height and crown radius of the sample trees were also measured. The field-surveyed variables were compared to the closest detected tree, with an overall detection accuracy of 75.2%. The initial results of this analysis allowed us to hypothesize that a higher detection of tree may be expected with larger densities.
