However, by the same token, they may be susceptible to the deleterious effects of nutritional misinformation and fads. The results suggest that people assimilate new ideas to improve their dietary habits. Also, large households' nutrition intake increases in response to nutritional information, whereas that of small households is mostly unaffected. The disaggregated analysis reveals that the nutrition intake of females responds to nutritional information, but that of males does not. Results show that acquiring nutritional information significantly improves households' intake of fats, iron, and selenium but does not affect the intake of proteins, carbohydrates, and zinc. We utilize propensity score matching to analyze cross-sectional data collected from 915 rural households in China. Accordingly, focusing on the three macronutrients (proteins, fats, and carbohydrates) and three critical micronutrients (iron, zinc, and selenium), we analyze the effects of nutritional information acquired from relatives, neighbors, and friends on rural households' nutrition intake. They also tend to have strong social ties, exhibit high social connectedness, and lean on relatives, neighbors, and friends for support and information. Rural communities in low- and middle-income countries are vulnerable to malnutrition. The results presented here are a first step towards quantitative estimations of the ecological damage caused by different antibiotics. We also estimated the NNH: replacing amoxicillin/clavulanate or cefazolin with cefuroxime would yield ceftazidime-resistance in one more patient for every 8.5 (95% CI 4.66-32.14) or 7.6 (95% CI 5.1-17.3) patients re-hospitalized in the following year.Ĭonclusions: Our results indicate that treatment with amoxicillin/clavulanate or cefazolin is preferable to cefuroxime, in terms of future collateral resistance. Results: Cefuroxime induced significantly higher resistance to ceftazidime than amoxicillin/clavulanate or cefazolin: the marginal OR was 1.76) 95%CI 1.16-2.83) compared to amoxicillin/clavulanate, and 1.98 (95%CI 1.41- 2.8) compared to cefazolin The RD was 0.118 (95%CI 0.031-0.215) compared to amoxicillin/clavulanate, and 0.131 (95%CI 0.058-0.197) compared to cefazolin. Logistic regression and g-formula (standardization) were used to estimate the odds ratio (OR), risk difference (RD), and number needed to harm (NNH). A 1:1 matching was performed for each patient in the amoxicillin/clavulanate and cefazolin groups, to a single patient from the cefuroxime group, yielding 185:185 and 298:298 matched patients. Patients were restricted to those treated with either amoxicillin/clavulanate, cefazolin, or cefuroxime and re-hospitalized with a positive bacterial culture during the following year. Methods: A retrospective analysis of patients with positive bacterial cultures hospitalized in an Israeli hospital during 2016-2019 was conducted. This study compared the effects of treatment with amoxicillin/clavulanate or cefazolin, compared to cefuroxime, on future resistance to ceftazidime among hospitalized patients. Its use should be encouraged in modern epidemiologic teaching and practice.īackground: Quantitative estimates of collateral resistance induced by antibiotic use are scarce. The g-computation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU. In our illustrative example, the effect (risk difference ) of a higher education on angina among the participants who indeed have at least a high school education (ATT) was -0.019 (95% CI: -0.040, -0.007) and that among those who have less than a high school education in India (ATU) was -0.012 (95% CI: -0.036, 0.010). The estimates for ATT, ATU and average treatment effect (ATE) were of similar magnitude, with ATE being in between ATT and ATU as expected. To obtain marginal effect estimates for ATT and ATU we used a three-step approach: fitting a model for the outcome, generating potential outcome variables for ATT and ATU separately, and regressing each potential outcome variable on treatment intervention. In this paper we illustrate the steps for estimating ATT and ATU using g-computation implemented via Monte Carlo simulation. Average treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projection of potential outcomes in a target (sub-) population.
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