Covariates Sample Clauses

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 birth94,95, age of marriage95,96, maternal education38,97, gender composition of living children98, birth order of index pregnancy38,94,99, caste89, religion89, and household wealth quintile89.

Covariates. Participants reported their gender identity, race/ethnicity, grade, difficulty falling or staying asleep, change in caffeine use, existence of a parent-imposed bedtime, and the time of their earliest synchronous (live) class. Individuals reporting a gender identity other than male or female were excluded from analysis due to small sample sizes. Race/ethnicity was coded as non-Hispanic white, non-Hispanic Black, Hispanic, non- Hispanic Asian, and non-Hispanic other, which included multiracial students and those who did not provide their race/ethnicity. Grade was categorized into middle school (grades 6-8), early high school (grades 9-10), and late high school (grades 11-12). Options for school closure status included open (with regular or limited class schedule), closed with instruction (synchronous, asynchronous, or a combination of the two), and closed with no instruction. This manuscript includes only those students who attended schools that were closed and providing instruction at the time of the survey. Participants reported how often they experienced trouble going to sleep or staying asleep in the past two weeks (never, once, twice, several times, or every day/night). Change in caffeinated beverage consumption was categorized as no caffeine use, using more caffeine during school closures, and using less caffeine during school closures. Participants noted the existence of a parent-set bedtime in response to a question on their reason for going to bed (response option: “My parents have set my bedtime”). Earliest live class time was categorized as before 8:30 a.m., 8:30-9:00 a.m., 9:15 a.m. or later, and no designated start time. Country of residence was obtained from Alchemer’s location data, and individuals taking the survey from outside the United States were excluded from analysis. Statistical Analyses Descriptive statistics (means, standard deviations, frequencies, and proportions) were examined for the full sample as well as by grade category (middle school, early high school, or late high school). One-way ANOVA and chi-square tests were performed to assess statistically significant differences in sleep outcomes by grade category. Additionally, chi-square tests were conducted to determine whether sleepiness during school activities differed by school start time. Analyses included seven multivariate regression models (M) estimating the relationship between school start time and sleep outcomes. Five continuous outcome variables (bedtime [M1], wake ti...

Covariates. We include men’s and women’s characteristics as covariates based on a priori review of the literature14,22. The number of husband’s inequitable views was added as a control variable to provide a baseline metric to the couples’ agreement variable discussed above. In addition, characteristics reported by the women include work status, parity,† marriage type, religion*, age, and education. Woman’s work * Note, the original survey did have a question about seeking medical care in general, but it was only administered in the spoken language Hausa so response rates were low. † Highly correlated with husband’s responses status was assessed using the yes/no question: “As you know, some women take up jobs for which they are paid in cash or kind. Others sell things, have a small business or work on the family farm or in the family business. In the last seven days, have you done any of these things or any other work?” We created an indicator variable for yes responses. Parity was calculated by summing the number of living children residing with the woman, number of living children residing away from the woman, and number of children that died. Due to a high prevalence of polygyny in Nigeria23, we also controlled for marriage type using an indicator variable. Xxxxxxxx was assessed from the woman’s perspective based on “yes” responses to the question, “Besides yourself, does your husband/partner have any other wives?”* Religion was dichotomized to Muslim and Christian due to a lack of variability in responses. Women’s age in years and education levels were included as well. We include several categorical variables comparing husband’s and wife’s relative differences in sociodemographic characteristics. To assess the relative age difference between husband and wife, we created a categorical variable with three levels: wife’s and husband’s ages within 5 years of each other, husband more than 5 but less than 10 years older than wife, husband 10 or more years older than wife. A similar categorical variable was created comparing husband and wife’s education levels: husband and wife comparably educated (both none/ Quranic†, primary, secondary, higher than secondary), husband more educated than wife, and wife more educated than husband. The household level factors include: household wealth quintiles and city. Wealth quintiles were created using principal component analysis of household assets and housing characteristics as described by Filmer and Xxxxxxxxx for the Demographic ...

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). 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. For individual characteristics of children and their caregivers as described in the model, we selected the factors based on our conceptual framework. These factors included: demographics (children’s gender, age, and race/ethnicity), respondents’ education, primary language spoken in the household, children’s special healthcare needs, children’s physical health status, and respondents’ physical health status, as well as their mental health status. Also measured in this study were family level features: housing conditions, which was measured as a dichotomous variable whether the family owned their home or not; household size, which asked the number of adults and the number of children living in the house; and family structure, which was categorized as “biological family,” “step family,” “single mother family,” and “other family types.” We also included a dichotomous indicator to further distinguish households under 100% of the Federal Poverty Level (FPL), because there is still a wide range of FPL within publicly insured children, and families in real “poverty” (<100% FPL) have minimal ability to pay for basic health care resources compared to those above 100% FPL. Finally, we took into account caregivers’ perception of community environment, including the presence of rundown housing, and whether or not they felt safe in the neighborhood. Both of these two community indicators were dichotomous. We maintained most of the original format of these variables and modified only variables that had very skewed distribution. For example, we combined the lowest two and highest two levels of the five initial levels of respondent’ physical and mental health status (from “poor,” “fair,” “good,” “very good” to “excellent”) and re-categorized it into three levels (from “less than average,” “average,” to “better than average”). A list of variables and more detailed information are provided in Table 1.

Covariates. Age, number of prenatal care visits, education, and race/ethnicity were included as potential effect modifiers or confounders based on information in the literature. Medicaid enrollment was used as a proxy measure for income. In Georgia, pregnant women are eligible for Medicaid coverage if they fall below 220% of the federal poverty level (9). All covariates are recorded on the birth certificate, and are self-reported. The Georgia birth certificate has payment categories of: Medicaid managed care, Champis, Medicaid, private insurance, other government assistance, self-pay/uninsured, and unknown. Unknown payment observations were set to missing and payment options were simplified into non-Medicaid or Medicaid, with Medicaid including women who paid using Medicaid or Medicaid managed care. Payment information was missing for 8.4% (n = 280) of the sample. Maternal age is recorded on the birth certificate as a continuous variable. Maternal age was separated into six categories for Tables 1 and 2: 18 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, and 40+. Maternal age was used as a continuous variable in the logistic regression models. Age information was missing for 6.4% (n = 214) of the sample. Prenatal care adequacy is measured on the birth certificate using the Kotelchuck Index, which is calculated based on the gestational age when prenatal care began and on the gestational age at delivery. The Kotelchuck Index is based on the assumption that the earlier prenatal care begins the better, and the index has four different levels: inadequate (received less than 50% of expected prenatal visits), intermediate (50% - 79%), adequate (80% - 109%), and adequate plus (110% or more) (22). Unknown prenatal care information was set to missing. Prenatal care information was missing for 28.1% (n = 935) of the sample. Maternal education is self-reported as the mother’s highest grade completed at the time of birth. Unknown education information was set to missing, and education was divided into four categories: less than 9th grade, 9th through 11th grade, High School/GED, and some college or higher. Education information was missing for 3.4% (n = 113) of the sample. Maternal race is recorded on the birth certificate as six categories: American Indian or Alaska native, Asian, black or African-American, multiracial, native Hawaiian or other, and white. Maternal ethnicity is recorded as Hispanic or non-Hispanic. For the purposes of this study, race and ethnicity were combined into a...

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. 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, controlling for a va...

Covariates. Health-relatedcovariates:Multimorbiditywas evaluatedin two ways. The firstof them was by means of self-reportof medicaldiagnosisof some conditionsassessedin the individualquestionnaire:arthritis,stroke,angina,diabetes,chronic lung disease,asthma, hypertensionor cataracts.The second was by applyingfour additionalalgorithmsto determine Statistical analysis Ethics statement Results

Covariates. For theoretical purposes, grounded in the findings from the literature review, several covariates were considered for controlling in data analysis to reduce the possibility of confounding. Such variables included age, social support, depression, and history of abuse. Social support was assessed using an 11-item scale with responses ranging from

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). 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. 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 We compared the descriptive statistics for four groups of respondents: