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Child Poverty

Child Poverty: A comprehensive term describing the complex socioeconomic conditions that contribute to impoverished living standards for children.
This encompasses factors such as limited access to healthcare, education, and basic necessities, often exacerbated by systemic inequalities.
Researchers utilizing PubCompare.ai can leverage data-driven insights to identify the most effective protocols and interventions to combat this global challenge, empowering them to make a real difference in the lives of vulnerable children.

Most cited protocols related to «Child Poverty»

1
Nationwide Childhood Obesity Study in Spain
The aim was to recruit 4508 children and their parents from 242 participating schools in the 17 autonomous communities. Ceuta and Melilla, two autonomous cities in North Africa with less than 0.8% of the total Spanish population aged 8–16 years, were not included for logistical reasons.
This cohort study is coordinated by the Gasol Foundation, whose aim is to reduce childhood obesity rates through the promotion of sports and PA, healthy eating, sleep quality and the emotional well-being of children, adolescents and their families in the USA and Spain. Field and scientific work is being performed together with 13 highly experienced research groups working at universities and research centres in several regions of Spain (online supplemental file 1). Selected educational centres first received an invitation letter signed by the president of the Gasol Foundation (Pau Gasol) and accompanied by support letters from the autonomous community’s departments of education and/or health and sports and from Spain’s Ministry of Education and Vocational Training; Ministry of Health, Consumer Affairs and Social Welfare; Council of Sports and High Commission against Child Poverty. In a second step, Gasol Foundation staff called the invited educational centres to confirm their interest in participating. The principal investigators at these centres are well-known experts in the investigation of the relationship between lifestyle and disease. In a third step, the regional coordinators of the PASOS project contacted the interested educational centres to introduce them to the study and invited them to participate. In the participating schools, parents (or legal guardians) were contacted by teachers designated by school administrators and received an envelope containing instructions to complete the requested documentation, two copies of the informed consent form, and two copies of the short questionnaires to be completed by an adult. When the school received a signed copy of the informed consent form, the child participant and family were included in the PASOS study. The study was approved by the Ethics Committee of the Fundació Sant Joan de Déu, Barcelona, Spain.
Gómez S.F., Homs C., Wärnberg J., Medrano M., Gonzalez-Gross M., Gusi N., Aznar S., Cascales E.M., González-Valeiro M., Serra-Majem L., Terrados N., Tur J.A., Segú M., Lassale C., Benavente-Marín J.C., Labayen I., Zapico A.G., Sánchez-Gómez J., Jiménez-Zazo F., Alcaraz P.E., Sevilla-Sanchez M., Herrera-Ramos E., Pulgar S., Bibiloni M.D., Sancho O, & Schröder H. (2020). Study protocol of a population-based cohort investigating Physical Activity, Sedentarism, lifestyles and Obesity in Spanish youth: the PASOS study. BMJ Open, 10(9), e036210.
Publication 2020
Administrators Adolescent Adult Child Child Poverty Community Health Education Emotions Ethics Committees Hispanic or Latino Legal Guardians Parent Pediatric Obesity
2
Prenatal ETS and Lead Exposure: ADHD Predictors
We examined multiple covariates and potential confounders for the association of prenatal ETS exposure and lead exposure with ADHD. Demographic variables included the child’s age, sex, race, and socioeconomic status [as measured by poverty-to-income ratio (PIR)]. PIR is the ratio of family income to the poverty threshold for the year of the interview. Children with PIR values < 1 are considered to be living below the poverty level. Health insurance coverage was also included as a covariate. In addition, a review of the literature suggested that preschool attendance, low birth weight, and ferritin levels (an indicator of iron status) should be considered potential confounders because of their prior documented associations with child behavioral problems and environmental toxicants [Knopik et al. 2005 (link); Kordas et al. 2004 (link); Mick 2002b ; National Institute of Child Health and Human Development (NICHD) 2003 (link); St. Sauver et al. 2004 (link); Wasserman et al. 2001 (link)]. Child’s birth weight and admission to a neonatal intensive care unit (NICU) were included as markers of perinatal distress.
Braun J.M., Kahn R.S., Froehlich T., Auinger P, & Lanphear B.P. (2006). Exposures to Environmental Toxicants and Attention Deficit Hyperactivity Disorder in U.S. Children. Environmental Health Perspectives, 114(12), 1904-1909.
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Publication 2006
Birth Weight Child Child, Preschool Child Poverty Disorder, Attention Deficit-Hyperactivity Ferritin Iron Problem Behavior
3
Longitudinal Study of Childhood Poverty in Four Countries
Young Lives is a longitudinal study of the causes and consequences of childhood poverty in Peru, Vietnam, India and Ethiopia(19 (link)). Within each country, the study follows a cohort of ~2000 children (there is also an older cohort of about half this size). The Young Lives Project used a multistage sampling strategy, whereby the first stage consisted of the selection of twenty sentinel sites (clusters) per country using a sampling methodology referred to as the Sentinel Site Surveillance System. In each country, the sentinel sites were selected in a semi-purposive manner based on socio-economic, demographic, geographic and policy variables that were relevant to the project. In Peru, Vietnam and Ethiopia, a nationwide sampling frame was used, whereas in India the sentinel sites were selected within the state of Andhra Pradesh. The sampling frame allowed for oversampling of poor areas and a mixture of urban and rural areas. In the second stage, within each sentinel site, households with a child in the target age range were enumerated and approximately 100 index children were selected according to comparable, but country-specific, protocols that were analogous to statistical random sampling. Refusal rates among selected households were <2% in all four countries and in the case of refusal, replacement sampling was used(19 (link)). These sampling methods resulted in a sample for each country that reflected the ethnic, geographic and religious diversity of the population, but that was not chosen to be directly nationally representative. Further details on the sampling strategy used within each country can be found in the Preliminary National Reports (accessible at http:/www.younglives.org.uk/). Data collection took place in 2002 (round 1, age 6 to 18 months), in 2006–2007 (round 2, age 4·5 to 5 years) and in 2009–2010 (round 3, age 7 to 8 years). Supine length (round 1) and standing height (rounds 2 and 3) were measured with length/height boards using standardized WHO methodology(20 ) and measurements precise to 1 mm. The length and height measurements were converted to Z-scores (HAZ) using WHO standards(21 (link)–23 ).
Lundeen E.A., Behrman J.R., Crookston B.T., Dearden K.A., Engle P., Georgiadis A., Penny M.E, & Stein A.D. (2013). Growth faltering and recovery in children aged 1–8 years in four low- and middle-income countries: Young Lives. Public health nutrition, 17(9), 2131-2137.
Publication 2013
Child Child Poverty Households Reading Frames
4
Evaluating Intervention Effects on Childhood Outcomes
At both ages 24 and 27, the overall distribution of participants in the intervention conditions did not significantly differ for those lost to attrition versus the analysis sample (χ22 = 2.16, p = .34 at age 24, and χ22 = 1.41, p = .50 at age 27); nor, among those retained in the analysis sample, did the distribution of participants in the intervention conditions differ with respect to gender (χ22 = .38, p = .83, and χ22 = .59, p = .74, respectively), ethnicity (Caucasian American vs. other; χ22 = .13, p = .94, and χ22 = .02, p = .99, respectively), or childhood poverty (χ22 = .23, p = .89, and χ22 = .33, p = .85, respectively).
Given the requirement that full intervention students attended project schools at some point in grades 1 through 4 and in grades 5 and 6, whereas some control students were added to the study at grade 5, it is important to rule out differences in residential stability, a potential threat to internal validity. Analyses comparing the full intervention and control groups found no significant differences in mean number of years living in Seattle by grade 6 (F = .61, p = .44 at age 24, and F = 1.83, p = .18 at age 27), mean number of residences lived in from age 5 to 14 years (F = 1.57, p = .21, and F = 1.56, p = .21, respectively), proportion of single-parent homes during grade 5 (χ2 = .11, p = .74, and χ2 = .02, p = .89, respectively), or living in a disorganized neighborhood at age 16 years (e.g., rundown housing, crime; χ2 = .47, p = .49, and χ2 = .13, p = .72, respectively). Differential school or teacher receptivity to intervention is also an unlikely threat to internal validity. Teachers in 6 of 8 participating schools during grades 1 through 4 were randomly assigned to either intervention or control classrooms. At fifth grade, newly eligible schools were matched demographically to early experimental schools, and each agreed to serve as a control or late intervention school depending on assignment. Additionally, during the course of the intervention, the Seattle school district used mandatory busing to achieve racial equality in schools, which substantially reduced the risk that outcomes observed reflected contextual or neighborhood differences, school demographic differences, or parent school-selection effects in the populations attending different schools.
An exception to the pattern of condition equivalence was the proportion of surveyed participants who reported at age 24 that their mothers were 19 years of age or less when they were born. Nine percent of the full intervention condition, compared to 21% of the control condition reported that their mothers were teens when they were born (χ2 = 8.56, p < .01). Having a teen mother was included as a covariate in all outcome analyses in this study.
Hawkins J.D., Kosterman R., Catalano R.F., Hill K.G, & Abbott R.D. (2008). Effects of Social Development Intervention in Childhood Fifteen Years Later. Archives of pediatrics & adolescent medicine, 162(12), 1133-1141.
Publication 2008
Adolescent Mothers Caucasoid Races Childbirth Child Poverty Crime Ethnicity Mothers Parent Population Group Residency School Teachers Student Teens Tooth Attrition
5
Long-term Evaluation of Seattle Social Development Program
The first phase of the SSDP longitudinal study began in 1981 with an experimental intervention initiated with all first-grade students in eight Seattle public schools serving high-crime neighborhoods. This early intervention was randomized at both the school and classroom level, as shown in Figure 1. In 1985, due to a change in funding, the study was expanded to a total of 18 matched Seattle public schools, adding new study conditions and additional control participants (Hawkins & Catalano, 2005 ). The longitudinal sample was established in 1985 from the population of 1,053 students entering Grade 5 in these 18 schools, 808 (77%) of whom assented, with their parents’ consent, to participate in the longitudinal study. Thereafter, all fifth-grade students in each school received either the intervention or no intervention according to their school’s intervention assignment. This resulted in a nonrandomized controlled trial with four conditions, two of which—the “full intervention” and control conditions—are the focus of this report. The full intervention group consisted of those who received at least two semesters of intervention—one in Grades 1 through 4 and one in Grades 5 and 6—with an average dose of 4.13 years of intervention exposure. The control group received no intervention from the project throughout. Other conditions did not meet these criteria and are not discussed in this report (a “late intervention” group and a “parent workshops only” group received intervention components during Grades 5 and 6 only, and 24 students could not be classified because they left participating schools before spending at least one semester). A study flowchart and group sample sizes are shown in Figure 1.
Of the 808 study participants, 49% are female; 47% are European American, 26% African American, 22% Asian American, and 5% Native American. Of these, 5% are Hispanic. Over 52% of the sample experienced childhood poverty as evidenced by participation in the National School Lunch/Breakfast Program between the ages of 10 and 12. Participants have been interviewed 15 times starting at age 10, with the most recent survey waves at ages 30, 33, 35, and 39 (the interview at age 35 used an abbreviated survey focusing on substance use and physical health). Retention of still-living participants averaged 88% from age 30 to 39 (37 participants were deceased by age 39).
Kosterman R., Hawkins J.D., Hill K.G., Bailey J.A., Catalano R.F, & Abbott R.D. (2019). Effects of Social Development Intervention in Childhood on Adult Life at Ages 30 to 39. Prevention science : the official journal of the Society for Prevention Research, 20(7), 986-995.
Publication 2019
African American American Indian or Alaska Native Asian Americans Child Poverty Crime Early Intervention (Education) Europeans Females Hispanics Parent Physical Examination Retention (Psychology) Student Substance Use Workshops

Most recents protocols related to «Child Poverty»

1
Measuring Health Disparities in DRC
A priori confounders, such as age, sex and geographic location, were included in descriptive and regression analyses. Other potential confounding variables were identified in the literature and based largely on variables representing social determinants of health and found by others to capture health disparities, such as chronic malnutrition (stunting), maternal education, wealth index and birth order.16 ,23 ,24 Malaria status (via blood smear) was included due to its association with host immunosuppression,25 (link) and the authors previously found that rural versus urban residence impacted measles vaccination coverage.26 (link) Finally, due to the history of conflict in the DRC, we created a dichotomous variable of children living in provinces experiencing high levels of conflict versus all other provinces. The 5 (old) provinces (Katanga, Maniema, Nord-Kivu, Orientale and Sud-Kivu) with the highest levels of human displacement in 2014 were classified as provinces experiencing high levels of conflict, as most of the human displacement (97%) was conflict-related.27 ,28 Due to the challenges inherent with directly measuring a household’s wealth, the DHS assumes that the economic state of a household is related to whether the household has access to certain amenities or services. Such variables can easily be included into a questionnaire or directly observed by the interviewer. Within the DHS, households are placed into quintiles according to household ownership of previously selected assets and via principal components analysis. Asset scores are standardized relative to a standard normal distribution and each household receives a summed score based on the standardized score for each asset. Individuals receive a score reflective of their household score, and the resulting quintiles make up the wealth index for the DHS survey.29 In this study, we dichotomized wealth index to compare the most disadvantaged individuals to all others in the sample, resulting in a wealth index that compares the poorest children to all others, or the lowest quintile children to those in the combined group of second lowest, middle, fourth lowest and highest quintile.
Collinearity diagnostic statistics were performed on all explanatory variables included in the final regression models, with acceptable results (tolerance for all variables was ≥ 0.75, variance inflation factor for all variables was ≤ 1.33) for all variables of interest.
Ashbaugh H.R., Cherry J.D., Hoff N.A., Doshi R.H., Mukadi P., Higgins S.G., Budd R., Randall C., Okitolonda-Wemakoy E., Muyembe-Tamfum J.J., Gerber S.K., Wells C, & Rimoin A.W. (2023). Reported History of Measles and Long-term Impact on Tetanus Antibody Detected in Children 9–59 Months of Age and Receiving 3 Doses of Tetanus Vaccine in the Democratic Republic of the Congo. The Pediatric Infectious Disease Journal, 42(4), 338-345.
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Publication 2023
BLOOD Child Child Poverty Diagnosis Homo sapiens Households Immune Tolerance Immunosuppression Interviewers Malaria Malnutrition Measles Mothers Vaccination Coverage
2
Measuring Household Poverty Levels
To measure SES, the child’s household poverty level was derived from parent responses about family income. Specifically, parents were asked, “What is the income level of the household that the child lives in? Responses were coded according to federal poverty levels (FPLs), “Household income 0–99% FPL,” “Household income 100–199% FPL,” “Household income 200–399% FPL,” or “Household income 400% FPL or greater/.”
Foster M.E., Choo A.L, & Smith S.A. (2023). Speech-language disorder severity, academic success, and socioemotional functioning among multilingual and English children in the United States: The National Survey of Children’s Health. Frontiers in Psychology, 14, 1096145.
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Publication 2023
Child Child Poverty Households Parent
3
Children's Emotional Experiences in School: A Qualitative Exploration
Children across schools were expected to report overlapping but different experiences, therefore schools were treated as case studies. This article documents findings from five interviews with children attending one school (Silver Birch)3, situated in a county identified as a social mobility ‘cold spot’, performing within the worst 20% of counties in England (Social Mobility & Child Poverty Commission, 2016 ). Table 1 summarises the school’s socio-demographic composition. In total, 15 children who volunteered (aged 9–11, M = 9.9; Table 1) were chosen to participate. Children were invited to create their own pseudonyms. The lead researcher prioritised ensuring the sample was as varied as possible in terms of demographic backgrounds (including a range of pupils of different ethnicities, eligibility/ineligibility for FSM, with/without SEN/EHCPs). Additionally, children chosen included a mix of pupils who were vocal/less vocal in wellbeing lessons, providing quieter pupils with opportunities to contribute in a smaller group setting. Nonetheless, particular groups (e.g., pupils with SEN/EHCPs) are not represented in the final sample, owing either to these pupils not volunteering to participate in the interview phase of the research, or an absence of these pupil groups at the class cohort-level (e.g., pupils from certain ethnic backgrounds). Findings should be interpreted with this in mind, given the present sample is missing accounts from particular disadvantaged groups (see Limitations).

Pupil demographics at Silver Birch compared to national averages

LevelSchool% pupils eligible for FSM% pupils with a SEN or Education, Health and Care Plan% pupils requiring SEN support% pupils with EAL
PrimarySilver BirchBelow averageAverageAverageAbove average

Free School Meals (FSM); Special Educational Needs (SEN); English as an Additional Language (EAL). Information derived from publicly available Government database

Participant information

NameGenderAgeEAL?SEN/EHPC?FSM?EthnicityInterview group
MayoFemale9NNNAny other mixed backgroundGroup 1: Year 5 Girls
GinaFemale9NNNWhite British
FionFemale9YNNAny other White background
WillMale9YNNAny other White backgroundGroup 2: Year 5 Boys
EricMale9NNNWhite British
AlexMale9NNNWhite British
BobMale9NNNAny other White backgroundGroup 3: Year 5 Boys
JohnMale9NNNWhite British
RobertMale9YNNAny other White background
ShannonFemale10NNNWhite BritishGroup 4: Year 6 Girls
MaisieFemale10NNNAny other White background
KelseyFemale10NNNWhite British
AsherMale10NNYAny other Black backgroundGroup 5: Year 6 Boys
OmanMale10NNNBangladeshi
LukeMale11NNNWhite British

Pseudonyms used for pupil names. Free School Meals (FSM); Special Educational Needs (SEN); Education, Health, and Care Plan (EHCP); English as an Additional Language (EAL). Information derived from school administrative data

Semi-structured interviews conducted by the lead researcher followed a six-part schedule4: (1) Introduction, reminding children of research aims, their right to withdraw and safeguarding procedures, (2) A warm-up exercise reminding children of the range of emotions they might experience at school, (3) Children’s experiences of ‘feeling good’ and ‘less good’ at school, (4) Children’s experiences of ‘doing well’ and ‘less well’, (5) Children’s experiences of learning goals5, and 5) Closing thoughts (opportunity for children to ask questions and sign-posting to mental health services).6 On average, interviews lasted 34-minutes.
Feelings individuals attribute to their experiences are key sources of meaning in phenomenology (Smith & Osborn, 2015 (link)). Pre-prepared follow-up questions funnelled children’s responses to better understand their feelings. Phenomenological inquiries additionally often use visual aids such as feelings grids to help children articulate their feelings (Koller et al., 2022 ). A warm-up exercise using post-it notes therefore supported children to identify and organise feelings into ‘good’ and ‘less good’ feelings they might experience in school prior to any interview questions. Children were familiar with a range of ‘good’ and ‘less good’ feelings having previously discussed them as part of pre-interview wellbeing lessons (Clarke & Hoskin, 2022 ) whereby a feelings grid was used. During the interview, children often referred to their post-it notes when asked follow-up questions like ‘how did you feel?’.
Clarke T, & Platt R. (2023). Children’s Lived Experiences of Wellbeing at School in England: a Phenomenological Inquiry. Child Indicators Research, 16(3), 963-996.
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Publication 2023
Betula Child Child Poverty Common Cold Eligibility Determination Emotions Ethnicity Feelings Mental Health Services Pupil Silver Social Mobility Special Education Thinking
4
Analyzing Disparities in Childhood Obesity
The descriptive analysis was used to summarize the sample's characteristics. Graphical methods were used to visualize how overweight/obesity in children differed by ethnicity over time. The probability values were determined using column proportion tests. We used Equiplot charts to illustrate the prevalence of overweight/obesity in each wealth quintile by sex and ethnicity for each survey year. The line between the prevalence of the first and last wealth quintiles shows the degree of dispersion; the longer the line, the greater the socioeconomic dispersion. The slope inequality index (SII) and concentration index (CIX) were then calculated for each survey year and group data to estimate wealth inequality in overweight/obesity by ethnicity (25 (link)). To analyze health inequalities across wealth quintiles, it is recommended that both absolute and relative measures of inequality be used simultaneously (26 ). When the results of both inequality measures are significant, inequality between quintiles is asserted. The SII is a weighted, absolute measure of inequality that uses a logistic regression to represent the absolute difference in estimated values of a health indicator between the poorest and richest quintiles, while controlling for all other wealth quintiles (25 (link), 27 (link)). The CIX is a weighted, relative measure of inequality that is related to the Gini coefficient. It calculates the magnitude and direction of health inequality. It is defined as twice the area between the concentration curve and the diagonal, ranging from 1 to 1. The concentration curve represents the cumulative percentage of the health variable relative to the cumulative percentage of the sample, ranked by socioeconomic status from the most disadvantaged group to the most advantaged group (9 (link), 13 (link)). In the CIX analysis, we used the Erreygers correction, as suggested by other researchers who have studied health inequalities (28 –30 (link)). Understanding and interpreting the SII and CIX is easy. With values ranging from −1 to 1, the SII or CIX is a composite description of inequality across the population. The index is zero when there is perfect equality. When the values are negative, children from the poorest quintile are most affected by overweight/obesity; When the values are positive, children from the richest quintile are most affected by overweight/obesity. The magnitude of the index reveals the level of inequality (25 (link)). The CIX allowed comparisons of wealth inequality between surveys for annualized change, plots of the CIX indices and 95% confidence intervals were made. When the CIX value of the annualized change is positive or negative, wealth inequality is said to have decreased or increased, respectively. The analyses were weighted and found to be appropriate for the complex NHANES survey design. Probability values for statistical tests, where 2-sided p-values <0.05 were considered significant. Analyses were performed using Stata (STATA Corp., LP, College Station, Texas), and graphical representations were created using GraphPad Prism 9.
Traore S.S., Bo Y., Kou G, & Lyu Q. (2023). Socioeconomic inequality in overweight/obesity among US children: NHANES 2001 to 2018. Frontiers in Pediatrics, 11, 1082558.
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Publication 2023
Child Child Poverty Ethnicity Obesity Pediatric Obesity prisma
5
Nationally Representative Childhood Anthropometrics
Our analyses focused on data from children under the age of five who took part in the NHANES from 2001–02 to 2017–18, a total of nine surveys. The NHANES is a study conducted by the Centers for Disease Control and Prevention (CDC) that gathers cross-sectional data on the health, nutrition, and health behavior of the civilian noninstitutionalized population in the United States. The survey used a multistage stratified cluster probability sampling approach, involving careful selection by geographic region, home composition, and person, to ensure a nationally representative sample. Participants in each survey were invited to engage in an interview in their homes, followed by physical examinations in a mobile examination center (MEC). The revision of subgroup proportions within the total population was taken into account during sample weighting methods (17 (link)). The databases and detailed information on the sampling procedure are freely available on the CDC website (18 ). In a brief, we only included participants who had available anthropometric measurements of height/length and body weight. To prevent the effect of unhealthy weights for length/height (19 (link), 20 (link)), children with a BMI-for-age z-score of less than −6.0 SD or greater than +6.0 SD ratio were excluded from the analysis (21 (link)). Children with missing values of the family income/poverty were also excluded from the analysis. In compliance with the NHANES protocol, informed consent was obtained from the parents/legally authorized representatives of subjects that are under 16. The study was approved by the National Center for Health Statistics' institutional review board (17 (link), 22 ).
Traore S.S., Bo Y., Kou G, & Lyu Q. (2023). Socioeconomic inequality in overweight/obesity among US children: NHANES 2001 to 2018. Frontiers in Pediatrics, 11, 1082558.
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Publication 2023
Body Weight Child Child Poverty Ethics Committees, Research Parent Physical Examination Population Health

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More about "Child Poverty"

Childhood Impoverishment, Youthful Destitution, Juvenile Hardship, Underprivileged Minors, Impoverished Youth, Economically Disadvantaged Children, Stata 17.0, SAS 9.4, Socioeconomic Determinants of Child Wellbeing, Systemic Inequities Affecting Juvenile Standards of Living, Ameliorating Conditions of Impoverished Children, Optimizing Healthcare and Educational Access for Underprivileged Youth, Data-Driven Interventions to Combat Child Poverty