Covariates. Individual level covariates also included in the model have each been shown to be associated with anemia, maternal health service utilization, or IFA receipt. These were maternal age at index birth29,30, age of marriage30,31, maternal education32,33, gender composition of living children34, birth order of index pregnancy29,32,35, caste19, religion19, and household wealth quintile19 (See Table 2.2).
Covariates. Age at Pregnancy was calculated using the date which a pregnancy-related ICD-9-CM code first showed up in the Medicaid claims data and mother’s date of birth; in cases with multiple dates of birth, the most recent observation was used. Age at pregnancy was then transformed into a dichotomous variable: 18.999 years old or younger was code as ‘1’ and 19 years old or older was coded as ‘2’, to account for the change in Medicaid eligibility criteria at that age. Race was a categorical variable including White, Black, Asian, American Indian/Alaskan native, native Hawaiian/Pacific Islander. White was coded as ‘1’, Black was coded as ‘2’, American Indian/Alaskan native was coded as ‘3’, Asian was coded as ‘4’, and native Hawaiian/Pacific Islander was coded as ‘5’. However, given the small sample of individuals who identified as Asian, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, only those who identified as black or white were included in the final analysis. Severity of CHD was initially coded into five categories based on a modified version of Marelli’s CHD hierarchy: Severe = ‘1’, Shunt = ‘2’, Shunt and Valve = ‘3’, Valve = ‘4’, and Other = ‘5’. This variable was then recoded, collapsing the Marelli-based five hierarchical categories into three groups: ‘1’= Severe, ‘2’ =mild/moderate, and ‘3’ = those CHD patients with an isolated 745.5 ICD-9-CM code (a code that is often used for non-CHD diagnoses). Urban-Rural Residence was computed using the Federal Information Processing Standard (FIPS) county codes to create a dichotomous variable with counties categorized as either urban coded as ‘1’ or rural coded as ‘2’. FIPS county codes were based on the 2010 Census of Population and Housing (48).
Covariates. Several independent variables that have been reported to be associated with depression or anxiety disorders were included in the logistic regression model. These variables are age, gender, race or ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, non-Hispanic other/multiple race), current physical limitation due to arthritis, and BMI (=weight[kg2]/height[m2]; <19 underweight; 19 -25 normal; 25-30 overweight; 30+ obese). Statistical Analysis Descriptive statistic analysis was first conducted to summarize the characteristics of the sample population. Crude prevalence rates and odds ratios of psychological distress were calculated by sample population characteristics. Chi-square tests were conducted to examine the differences between subgroups, including levels of moderate- to vigorous- activity, levels of strength activity, BMI groups, sex, race/ethnicity, and current physical limitation due to arthritis. Multivariable logistic regression analyses were performed to test the association between different levels of participation in physical activity and psychological distress. A significance level of α = 0.05 was used. Results
Covariates. The demographic characteristics that were captures included residential area, place of residence by region, wealth quintile, education level, age, birth order, and marital status. Beside place of residence by region being the independent variable, the other demographic characteristics were chosen in terms of confounding variables because they follow underlying principles and basis previously established secondary by the secondary revision of Xxxxxxxx’x theoretical framework.
Covariates. In addition to the key independent variable of interest, we controlled for several demographic measures, including those shown in the table below (Table 1). Table 1: Covariates Variable Name Variable Type Description Age Continuous Ranging from 12 to 100 at baseline and 11 to 99 at follow-up survey Gender Dichotomous Female or male Educational level Categorical Elementary school or Don’t know (DK) Junior high school Senior high school Undergraduate+ Current Occupational level Categorical Government Professional/technical Business person-employee Salesperson, no formal occupation, unemployed, or homemaker Medical staff, educator, or transportation personnel Business owner or other Statistical Analysis To examine whether the anti-tobacco media exposures were associated with respondent’s knowledge of SHS hazards, respondent’s smoking status, and household SHS exposure status, we conducted three logistic regression analyses. We conducted the regression analyses for four groups of respondents, which were P1 (N=1,016), P1 – Group 1 (N=562), P1 – Group 2 (N=454), and P2 (N=903) by using the following three models. The general regression model to predict respondent’s knowledge of SHS hazards: Respondent’s knowledge of SHS hazards = b0 + b1 (Anti-tobacco media exposures) + b2 (Person level covariates) + b3 (communities of respondent)+ ei, The general regression model to predict respondent’s smoking status and household SHS exposure status: Outcome variables = b0 + b1 (Anti-tobacco media exposures) + b2 (Respondent’s knowledge of SHS hazards) + b3 (Person level covariates) + b4 (communities of respondent)+ ei, ❖ Note: Outcome variables includes respondent’s smoking status and household SHS exposure status Results We compared the descriptive statistics for four groups of respondents:
Covariates. The demographic variables in our analyses included respondents’ age, race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Mexican American, Hispanic, or other), education (<high school, high school, or >high school), employment (employed or other), marital status (married, divorced/separated/widowed, single, or living with partner), household income (≤$24,999; $25,000-54,999; $55,000-74,999; ≥$75,000) body mass index (BMI, self-reported weight divided by height in square) and health insurance status (private, public, or none). Smoking status was categorized as current smokers (participants who had smoked cigarettes,
Covariates. As potential confounders, we assessed age of the child and parent as well as how far apart in age child and parent were (age difference). We also included parent and child sex and a variable indicating whether parent and child were the same sex (different sex). Lastly, we measured household SES based on family income and the educational attainment of both parents (for a detailed description see Xxxxxxx et al., 2020). Family income and educational level were first standardized and then averaged. If information from both parents was available, data were averaged. Five percent of parents reported having only completed elementary school, 14% completed lower vocational education, 36% secondary education, and 41% had completed university. The majority of parents reported that their family income was €45,000 or higher. For details, see Supplement (Table S1). Resting State fMRI Data acquisition A 3-Tesla Philips Achieva scanner (Philips Medical Systems, Best, the Netherlands) was used to acquire structural and functional imaging data. During the 7.33 minutes resting state scan, participants were asked to look at a black screen with a white fixation cross in the middle. We collected resting state for (200 volumes) at repetition time (TR) of 2200 ms and echo time (TE) of 30ms (matrix size = 80 x 79, field of view = 220). All structural scans were reviewed by a radiologist. No incidental/anomalous findings were reported.
Covariates. A robust set of relevant covariates was constructed for inclusion in our analytic models using baseline survey data. Covariates were selected for their potential relation to our seven outcomes of interest including noncustodial fathers’ demographic characteristics (i.e., age, race, and ethnicity), marital and cohabitation status, and socioeconomic status (i.e., highest level of education and earnings in the 30 days before baseline survey administration). Additional covariates include incidence of major severe depression, a father’s relationship quality with and involvement of their own biological father,4 NCPs having children with more than one partner, gatekeeping by any custodial parent or a custodial parent’s family or friends, and NCPs residence with a non-biological child of a romantic partner. Child-level covariates include the number of nonresidential minor children a father has, average nonresident child age, average relationship quality with nonresident children at baseline,5 and nonresident child sex. Finally, the CSPED treatment group and a father’s state of residence are included. The full set of covariates is listed in Table 1. Analytic Approach To assess the associations between incarceration among NCPs and their relationships with and financial investments in their nonresident children, we conducted a robust set of multivariate regression analyses. Our main estimation strategies are ordinary least squares and logistic regression modeling. All regression models are weighted with CSPED survey weights. 4This variable is coded as 0=not at all involved, 1=somewhat involved and fair or poor relationship, 2=somewhat involved and excellent/very good/good relationship, 3=very involved and fair/poor relationship, and 4=very involved and excellent/very good/good relationship. We chose this approach to be consistent with previous literature that has used the same strategy (e.g., Xxxxxxx et al., 2018; Xxx et al., 2023). 5Relationship quality with children is both an outcome in its own right and potentially very important to the other outcomes of interest, so we include it as a covariate in those models (but exclude it from the models where relationship quality is the outcome). Our analytic approach attempts to quantify the possible associations between different measures of a father’s history of incarceration and a variety of relational and financial outcomes. Our analysis focuses on the relationship between incarceration and our outcomes of interest, co...
Covariates. We included the following socioeconomic and demographic variables to adjust for confounding of the relationship between the schooling variables and prior year IPV: residence (urban versus rural), age measured continuously in years, relationship status (married, living together, previously partnered), work status (worked within the past year versus not working), number of children living at home measured continuously, witnessing father beat mother (yes, no), and a continuous score for household wealth. The DHS household wealth index is the standardized score (mean 0, standard deviation of 1) derived from the first principal component of a principal components analysis of recoded items measuring whether or not the household had a specified set of assets and amenities (Xxxxxxxx, 2004). Age of partner was not asked of all women in the previously partnered category and therefore was not included in the analyses. Statistical Analysis All descriptive and inferential analyses were conducted using the PASW version 18.0 statistical package for PC (PASW Statistics 18, 2009) and multivariate logistic regression was conducted using SAS version 9.2 (SAS Institute, 2009). Univariate analyses were conducted for all covariates, outcomes, and variables from which analytic covariates were derived to assess their completeness, distributions, and relative frequencies. Bivariate associations of all covariates were estimated to assess potential co- linearity among these variables. Variables statistically associated with our outcome in the bivariate analysis were included in the multivariate model. Because the outcome variable, prior year IPV (y), for each individual is binary, multivariate logistic regression analysis was used with strata at the regional level and clustering at the primary sampling unit (PSU) to estimate the adjusted associations of explanatory variables on the log odds of having experienced IPV in the prior year. The SAS survey logistic procedure fits linear logistic regression models for survey data using the maximum likelihood method (Binder, 1983; XxXxxxxxx, 1989). The procedure incorporates complex survey sample designs, including designs with stratification, clustering, and unequal weighting for statistical inferences (SAS Institute I, 2004). The model used to estimate the adjusted effects of covariates on the adjusted log odds of having experienced IPV in the prior year was:
Covariates. The stratification variable of presence/absence of clinical symptoms of trichomoniasis at baseline will be included as a factor in the primary efficacy analysis.
