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. 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. Age Gender RIDAGEYR RIAGENDR Best age in years of the sample person at time of HH screening. Individuals 85 and over are topcoded at 85 years of age. Gender of the sample person Categorized into pre- school and primary school versus teen and college age Coded the same as 2 levels 2 levels 0-12 years / 13-19 years Male / Female Race/Ethnicity RIDRETH1 Reported race or ethnicity NHANES Coded the same as 5 levels Non-Hispanic White / Non- Body mass index RIDAGEMN Best age in months at date of NHANES Variables entered into 3 levels Hispanic Black / Mexican American / Other Hispanic / Other Race - Including Multi Racial <5th percentile / ≥5th RIAGENDR screening for individuals under 85 years of age. Gender of the sample person CDC's SAS macro for children's BMI percentile but <85th percentile / ≥85th percentile BMXHT Standing Height (cm) Poverty income BMXWT BMXHEAD INDFMPIR Weight (kg) Head Circumference (cm) Poverty income ratio (PIR) - a PIR was assigned 3 3 levels ≤1 / >1 but less than 5 / ≥5 ratio Attached Home Age of Home Presence of HOQ011 HOQ040 HOQ240 ratio of family income to poverty threshold Is your home . . .{Mobile home or trailer / One family house detached / One family house attached / Apartment / Dormitory}? When was this {mobile home/house/building} originally built? In the past 12 months, have categories for poor, medium and high Categorized into houses that are standalone residences and those they share walls, utilities, etc. Original 6 levels were dichotomized If subjects answered yes 2 levels 2 levels 2 levels Attached / Detached Before 1959 / 1960 or later Yes / No Cockroaches Presence of HOQ230 you seen any cockroaches in your home? In the past 12 months, has to this question they were considered to have cockroaches in their home If subjects answered yes 2 levels Yes / No Mold/Mildew your home had a mildew odor or musty smell? to this question they were considered to have mold/mildew in their home
Covariates. Age was included as a continuous variable. Other categorical sociodemographic variables include gender (male, female), marital status (recatagorized into a binary “married/not married” variable), urbinicity (counties with a USDA Rural Urban designation of metro were recategorized as “urban”, counties with a nonmetro designation were recategorized as “rural”), education (less than high school, high school graduate, some college, college graduate or more), race (Hispanic, Non-Hispanic White, African American or Black, and Other), income category ($20,000 to < $35,000, $35,000 to < $50,000, $50,000 to < $75,000, $75,000 or More), and having a regular healthcare provider (yes, no) All analysis was conducted in SAS-callable SUDAAN, version 11.0.1, to account for the multistage sampling design and sample weights. A full-sample weight was used to calculate population and subpopulation estimates. 50 replicate weights, calculated using the delete one jackknife (JK1) replication method, were used to calculate standard errors. We conducted our analysis in two phases - first, an analysis of the entire sample and second, an analysis stratified by where respondents first search for health information (internet or other). Descriptive statistics were examined for all outcome, predictive, and demographic variables. Bivariate analysis, including chi square tests and t-tests, were conducted to determine if there were any significant independent associations between the three outcome variables and predictor variables. Weighted logistic regression was conducted separately for each outcome variable with each variable measuring internet health activity. Unadjusted analysis was conducted to generate odds ratios (OR) and 95% confidence intervals (95% CI). Adjusted odds ratios (aOR) with 95% CI were calculated by including sociodemographic covariates in the model to control for any confounding by sociodemographic characteristics. All analyses were conducted at a significance level of alpha = 0.05.
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. All information in the matched multiple birth data set is based on what is documented on the birth certificate or death certificate of the individual infant. Maternal race was categorized as white, black, or other. Maternal age was classified into 5 different categories: <20 years, 20-24 years, 25-29 years, 30-35 years, and >35 years. Maternal education was categorized as (1) less than high school, (2) completed high school, (3) some college, (4) completed college or greater, and (5) unknown. Gestational age at delivery is reported based on either mother’s last menstrual period or clinical ultrasound, depending on the method recorded on the birth certificate. Gestational age was categorized into 4 classes: <28 weeks, 28-32 weeks, 33-36 weeks, or ≥37weeks. The data also reported information on any congenital anomalies that were recorded on the infant birth or death certificate. The data contains specific information about 21 different congenital anomalies, as well as a category for any other unknown or unspecified anomalies. Distributions were calculated for each anomaly separately, and a new variable created to represent the presence of any type of anomaly. Presence of congenital anomaly was further classified as present, not present, or unknown or unspecified. Maternal parity was categorized as either nulliparous or multiparous. Adequacy of prenatal care was classified as either (1) early prenatal care (entry in to prenatal care during the first trimester), (2) late prenatal care (entry into prenatal care after the first trimester), or (3) no prenatal care or unknown. Placental abruption was classified as present, not present, or unknown.
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. Due to the small sample size, the impact of baseline characteristics on study outcomes will not be explored, and no subgroup analyses will be performed in the primary analyses of safety and efficacy endpoints. Covariates could be included in secondary analyses for these purposes and will be defined in the statistical analysis plan.
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. 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
