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Our study also documented that the model for underweight showed non-significant association with rainfall as compared to models for stunting.
Unlike the model for underweight status, smoking status was not an important independent factor for being overweight/obese by either definition.
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The crude (Model 1) and fully adjusted OR (Model 4) for non-participation in mammographic screening for underweight as compared to women with normal BMI was 2.19 (1.26-3.82) and 2.24 (1.27-3.96), respectively (Table 2).
The R-square values for models on underweight were very small and vary over the three agro ecologies.
The risks of mortality for underweight, overweight, and obesity were of similar magnitude in models that analysed smokers and non-smokers separately, as well as in models with multivariate adjustment.
Odds Ratios were calculated with logistic regression models to estimate the odds of having each individual obstetrical or perinatal maternal complication, with predictor variables for underweight, overweight and obese women using healthy weight women as the reference group.
The accuracy of screening for underweight (thinness), overweight, and obesity using self-reported data was also tested.
However, the optimal GWG for underweight and obese women was outside the IOM recommended range.
Weight z-score improved by a median 0.4 units (p < 0.001), especially for underweight children less than 18 months old.
In contrast, underweight women with a history of restrained eating behaviors gained less weight compared to underweight women without those behaviors.Restrained eating behaviors were associated with weight gains above the Institute of Medicine's recommendations for normal, overweight, and obese women, and weight gains below the recommendations for underweight women.
We considered that in combination with the stretched out pose and heavy eye makeup, the model looked underweight in the pictures".
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CEO of Professional Science Editing for Scientists @ prosciediting.com