Descriptive analyses Sample Clauses

Descriptive analyses. ‌ For descriptive epidemiological analyses, we will use basic statistical tools such as calculation of means, medians, standard deviations etc. for continuous variables and percentage calculations for categorical variables. Parametric and non-parametric statistical tests will also be used such as Chi-square, Student's t, Xxxx-Xxxxxxx Xxxxxxxx, ANOVA etc.
Sample 1
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Descriptive analyses. Of the 187 religious leaders surveyed for the Malawi Religion Project and described in Table 1, over 95% of respondents were male and only 29% had achieved at least some secondary education. Respondents resided in rural communities from one of three districts in Malawi: Rumphi in the north (36%), Mchinji in the central region (29%), and Balaka in the south (35%). The average age of the religious leaders was 47 years and they led congregations with an average regular attendance of 38 adult members, with a highest reported congregation size of 370 adult members. The affiliated religious tradition of each leader was categorized into six major distinctions, the most frequent being Mission Protestant (21%), African Independent Congress (20%), and New Mission Protestant (18%).
Sample 1
Descriptive analyses. All data analysis was conducted using SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA). A univariate analysis was performed to describe the distribution of age, sex, race, ethnicity, insurance type, clinical setting, and prior antibiotic use for the four defined AGE outcome groups (overall AGE, norovirus-associated AGE, rotavirus-associated AGE, and non- norovirus/non-rotavirus AGE) and healthy controls. Bivariate analyses were conducted to examine the associations between potential confounding demographic and epidemiologic variables with norovirus-associated AGE and prior antibiotic use status. Potential confounding variables were identified for all AGE outcomes, but differences in potential models were negligible for the outcome of interest. For the sake of comparability, a single model form was selected: the norovirus-associated AGE group. The variables considered included age at enrollment, sex, race, ethnicity, household income, insurance type, contact with an AGE affected person inside the household, contact with an AGE affected person outside the household, contact with someone who has traveled outside the U.S. in the past 7 days, travel outside the U.S. in the past 7 days, daycare attendance, season of enrollment (October-April vs. other), whether the child was ever breastfed, and whether the child is currently breastfeeding. Differences in these variables by disease outcome and exposure status were analyzed using chi-square tests for significance. Modeling Strategy Multiple logistic regression was performed to calculate odds ratios and 95% confidence intervals for the association between antibiotic use in the 3 months prior to enrollment and overall AGE, norovirus-associated AGE, rotavirus-associated AGE, and non-norovirus/non- rotavirus AGE. The model identification process was performed for norovirus-associated AGE and was checked for the other outcomes by comparing the odds ratios obtained using the exposure-only model and the full model selected for norovirus-associated AGE. If the odds ratio of the exposure-only model was within 10% of the odds ratio found using the model identified for norovirus-associated AGE, then the norovirus-associated AGE model was used for that outcome. Multiple logistic regression was also performed using different cutoffs for days since last antibiotic dose prior to enrollment (less than or equal to 3 weeks, greater than 3 weeks, and unknown time frame) to further assess the relationship between proximit...
Sample 1
Descriptive analyses. Sample characteristics at 25 week gestation One hundred and twenty-five pregnant women participated in the study. Of those, 58 (46.4%) were healthy controls, free from mental health diagnosis and 67 (53.6%) were cases, with a current DSM-IV diagnosis of Major Depressive Disorder (single episode, code 296.2x or recurrent, code 296.3x). Basic maternal socio-demographic characteristics at 25 weeks gestation are shown in Table 4. The majority of women (65.6%) were of white ethnic origin and their mean age was 31.12 years. Most women (78.4%) pursued a higher education and were employed (62.4%). More than half of the women (65.6%) were married or cohabiting and the majority were primiparous (81.6%). Table 4. Socio- demographic characteristics of the sample at 25 weeks gestation Mother’s age (years) Mean (S.D.) 31.1 (6.0) Range 18-46 Maternal ethnicity (%) White 82 (65.6) Black and minority 43 (34.4) Maternal qualifications (%) GCSE or lower 27 (21.6) A level or higher 98 (78.4) Maternal employment status (%) Working outside the home 78 (62.4) No working outside the home 47 (37.6) Marital status (%) Married or cohabiting 82 (65.6) Single or with a partner living out 43 (34.4) Parity (%) Primiparous 102 (81.6) Multiparous 23 (18.4) Socio-demographic characteristics at 25 week gestation: healthy and depressed women
Sample 1
Descriptive analyses. Neonatal outcomes in healthy and depression groups
Sample 1
Descriptive analyses. Full demographic characteristics of the study population stratified by NDVI quintiles are presented in Table 2. The mean NDVI value within 250m of the maternal residence is 0.358. The lowest NDVI quintile has the highest proportion of LPTB babies, however, the association between LPTB and NDVI does not appear to be linear and is not significant. As greenness increases, the proportion of White women increases while the proportion of every other race declines. NDVI is significantly associated race, as well as all other individual and neighborhood- level covariates.
Sample 1
Descriptive analyses. Mean and proportion were used to describe the socio-demographic characteristics and sexual behaviors at baseline and follow-up visits. Crude HIV incidence was calculated as follows: number of newly HIV positive cases / number of person-years of follow-up. Associations between socio-demographic characteristics and sexual behaviors and HIV strain were assessed using the Xxxxxxx’x xxx-square test. Variables associated with the outcome with a p-value ≤0.05 were considered to be significant. Bivariate and multivariate analysis To identify behavioral risk factors and socio-demographic characteristics associated with HIV strain, variables and relevant interactions with a p-value ≤0.05 for CRF01_AE strain in bivariate logistic regression analysis were entered into a backward stepwise multivariate logistic regression. Due to the small analytic sample of non-CRF01_AE seroconverters, variables and relevant interactions with a p-value of ≤0.25 in the bivariate analysis were included in a backward stepwise multivariate logistic regression model. Xxx proportional hazard analysis using a competing risk model was used to evaluate bivariate and multivariate behavioral risk factors and laboratory variables for incident HIV infection by subtype to identify independent risk factors for incident HIV infection. RESULTS
Sample 1
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Descriptive analyses. The analytic sample (N=1740) included participants enrolled from April 2006 till May 2012. We included a total of 188 seroconverters in this analysis: 154 were CRF01_AE strain and 34 were non-CRF01_AE strain. As shown in Table 2, the mean age of CRF01_AE seroconverters was 28 years (age range: 18-52 years), 41.6% had university education, and 68.2% lived away from the family. During the 4 months prior to seroconversion, 7.1% reported binge drinking, 13.0% used drugs, 11.0% used ‘club’ drugs, 12.3% used drugs to enhance sexual pleasure, 9.1% inhaled nitrates and 12.3% used erectile dysfunction drugs. During the same time period, six or more sexual partners was reported by 33.1% of CRF01_AE seroconverters, intermittent condom use by 54.5%, anal sex position (receptive only or both) by 74.0%, having an Asian partner by 9.1%, having a foreign partner by 8.4%, having been coerced into sex by 20.1%. In the 4 months prior to seroconversion, having paid money for sex was reported by 9.1% of CRF01_AE seroconverters, received money for sex by 13.0%, and engaged in group sex by 24.0%. The mean age of non-CRF01_AE seroconverters was 27 years (age range: 19-37 years), 50.0% had university education, and 61.8% lived away from the family (Table 2). During the 4 months prior to seroconversion, 8.8% reported binge drinking, 20.6% used drugs, 17.6% used ‘club’ drugs, 20.6% used drugs to enhance sexual pleasure, 17.6% inhaled nitrates and 11.8% used erectile dysfunction drugs. During the same time period, six or more sexual partners was reported by 35.3% of non-CRF01_AE seroconverters, intermittent condom use by 50.0%, anal sex position (receptive only or both) by 29.4%, having an Asian partner by 11.8%, having a foreign partner by 20.6%, having been coerced into sex by 20.6%. 8.8% paid money for sex, 2.9% received money for sex, and 29.4% engaged in group sex in the 4 months prior to seroconversion. Prevalence of sexually transmitted infections Rectal N. gonorrhoea and C. trachomatis was detected in 13.6% and 20.1% of CRF01_AE seroconverters, respectively. Laboratory evidence of past HAV infection, HBV surface antibody, HBV core antibody, HCV, HSV-1, and HSV-2 infection was detected in 26.0%, 4.5%, 58.8%, 44.4%, 1.3%, 64.3%, and 21.4% of CRF01_AE seroconverters, respectively. 8.4% tested positive for a history of T. pallidum. Rectal N. gonorrhoea and C. trachomatis was detected in 14.7% and 20.6% of non-CRF01_AE seroconverters. Past HAV infection, HBV surface antibody and H...
Sample 1

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