The Child Opportunity Index is calculated based on Education, Health & Built Environment and Neighborhood Social & Economic Opportunity indicators.

Background: Cancer-related cognitive impairment in survivors of pediatric brain tumors is a public health concern, yet studies seldom explore the role of social determinants of health beyond treatment effects. We investigated the influence of neighborhood resources using the Child Opportunity Index (COI) on neuropsychological functioning in survivors. Methods: Intelligence (IQ) and untimed reading and math proficiency were assessed retrospectively in 190 survivors. Multiple regression analyses explored associations among the COI composite and indices (Education, Health-Environment, Social-Economic) and neuropsychological outcomes, controlling for cumulative neurological risk and treatment exposures (Neurological Predictor Scale; NPS) and parental education. Results: Performance was on average within normal limits: IQ (M = 94.08, SD = 15.85, d=.37) with 13.4% of scores below impairment thresholds; reading (M = 95.08, SD = 17.36, d=.28) and math calculation (M = 91.84, SD = 18.82, d=.43) with 16.92% and 20.63% of scores below impairment thresholds, respectively. Each COI domain predicted reading and IQ after controlling for NPS, parental education, and age at diagnosis; however, the Education domain was the only significant predictor of math outcomes. Conclusion: The COI domains significantly predicted IQ and untimed academic skills in survivors, revealing the critical role of neighborhood resources on cognition above and beyond parental education and treatment factors. This is among the first studies to illuminate the influence of neighborhood resources on cognition in survivors. Future research should examine neighborhood context, an understudied construct, with importance in the move toward precision medicine.

ObjectiveTo describe the development of an area-level measure of children's opportunity, the Ohio Children's Opportunity Index (OCOI).Data Sources/Study SettingSecondary data were collected from US census based-American Community Survey (ACS), US Environmental Protection Agency, US Housing and Urban Development, Ohio Vital Statistics, US Department of Agriculture-Economic Research Service, Ohio State University Center for Urban and Regional Analysis, Ohio Incident Based Reporting System, IPUMS National Historical Geographic Information System, and Ohio Department of Medicaid. Data were aggregated to census tracts across two time periods.Study DesignOCOI domains were selected based on existing literature, which included family stability, infant health, children's health, access, education, housing, environment, and criminal justice domains. The composite index was developed using an equal weighting approach. Validation analyses were conducted between OCOI and health and race-related outcomes, and a national index.Principal FindingsComposite OCOI scores ranged from 0–100 with an average value of 74.82 (SD, 17.00). Census tracts in the major metropolitan cities across Ohio represented 76% of the total census tracts in the least advantaged OCOI septile. OCOI served as a significant predictor of health and race-related outcomes. Specifically, the average life expectancy at birth of children born in the most advantaged septile was approximately 9 years more than those born in least advantaged septile. Increases in OCOI were associated with decreases in proportion of Black (48 points lower in the most advantaged vs. least advantaged septile), p < 0.001) and Minority populations (54 points lower in most advantaged vs. least advantaged septile, p < 0.001). We found R-squared values > 0.50 between the OCOI and the national Child Opportunity Index scores. Temporally, OCOI decreased by 1% between the two study periods, explained mainly by decreases in the children health, accessibility and environmental domains.ConclusionAs the first opportunity index developed for children in Ohio, the OCOI is a valuable resource for policy reform, especially related to health disparities and health equity. Health care providers will be able to use it to obtain holistic views on their patients and implement interventions that can tackle barriers to childhood development using a more tailored approach.

Abstract

The Opportunity Atlas has collected contextual data by county and tract. Rather than providing contextual socioeconomic data of where people currently live, the data represents average socioeconomic indicators (e.g., earnings) of where people grew up.

Documentation

A core element of Population Health Science is that health outcomes can only be fully understood when they are studied within their context. Therefore, we have a copy of The Opportunity Atlas, a dataset that provides socioeconomic data by county and tract.

Several studies have shown that especially childhood neighborhoods drive adult outcomes and that residential areas lived in through adulthood have much smaller effects. The focus of the Opportunity Atlas is therefore on contextual data of where people grew up:

%3E Traditional measures of poverty and neighborhood conditions provide snapshots of income and other variables for residents in an area at a given point in time. But to study how economic opportunity varies across neighborhoods, we really need to follow people over many years and see how one’s outcomes depend upon family circumstances and where on grew up. The Opportunity Atlas is the first dataset that provides such longitudinal information at a detailed neighborhood level. Using the Atlas, you can see not just where the rich and poor currently live – which was possible in previously available data from the Census Bureau – but whether children in a given area tend to grow up to become rich of poor. This focus on mobility out of poverty across generations allows us to trace the roots of outcomes such as poverty and incarceration back to where kids grew up, potentially permitting much more effective interventions.

As such, The Opportunity Atlas data provides a rich source of data for researchers who wish to overlay health data with contextual data.

Methodology

Three sources of Census Bureau are linked to compute the data

1. The 2000 and 2010 Decennial Census short form
2. Federal income tax returns for 1989, 1994, 1995, 1998-2015
3. The 2000 Decennial Census long form and the 2005-2015 American Community Surveys (ACS).

%3C!-- --%3E

20.5 million Americans born between 1987-1983 are sampled from these data and mapped back to the Census tracts they lived in through age 23. After that step, a range of outcomes are then estimated for each of the 70,000 tracts. In order to comply with federal data disclosure standards and protect the privacy of individuals no estimates in tracts with 20 or fewer children are published and noise (small random numbers) is added to all the estimates.

For more information on the data collection and methodology, please visit:

Website

Documentation

Data availability

Some variables are available for counties only. The table below gives you an overview. Open the table in a new tab for a larger view.

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This dataset contains replication files for "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment" by Raj Chetty, Nathaniel Hendren, and Lawrence Katz. For more information, see https://opportunityinsights.org/paper/newmto/. A summary of the related publication follows. There are large differences in individuals’ economic, health, and educational outcomes across neighborhoods in the United States. Motivated by these disparities, the U.S. Department of Housing and Urban Development designed the Moving to Opportunity (MTO) experiment to determine whether providing low-income families assistance in moving to better neighborhoods could improve their economic and health outcomes. The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing). Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults. However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success. In this study, we present a new analysis of the effect of the MTO experiment on children’s long-term outcomes. Our re-analysis is motivated by new research showing that a neighborhood’s effect on children’s outcomes may depend critically on the duration of exposure to that environment. In particular, Chetty and Hendren (2015) use quasi-experimental methods to show that every year spent in a better area during childhood increases a child’s earnings in adulthood, implying that the gains from moving to a better area are larger for children who are younger at the time of the move. In light of this new evidence on childhood exposure effects, we study the long-term impacts of MTO on children who were young when their families moved to better neighborhoods. Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market. For older children (those between ages 13-18), we find that moving to a lower-poverty neighborhood has a statistically insignificant or slightly negative effect. More generally, the gains from moving to lower-poverty areas decline steadily with the age of the child at the time of the move. We do not find any clear evidence of a “critical age” below which children must move to benefit from a better neighborhood. Rather, every extra year of childhood spent in a low-poverty environment appears to be beneficial, consistent with the findings of Chetty and Hendren (2015). The MTO treatments also had little or no impact on adults’ economic outcomes, consistent with previous results. Together, these studies show that childhood exposure plays a critical role in neighborhoods’ effects on economic outcomes. The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls. Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains. Our estimates imply that moving a child out of public housing to a low-poverty area when young (at age 8 on average) using a subsidized voucher like the MTO experimental voucher will increase the child’s total lifetime earnings by about $302,000. This is equivalent to a gain of $99,000 per child moved in present value at age 8, discounting future earnings at a 3% interest rate. The additional tax revenue generated from these earnings increases would itself offset the incremental cost of the subsidized voucher relative to providing public housing. We conclude that offering low-income families housing vouchers and assistance in moving to lowerpoverty neighborhoods has substantial benefits for the families themselves and for taxpayers. It appears important to target such housing vouchers to families with young children – perhaps even at birth – to maximize the benefits. Our results provide less support for policies that seek to improve the economic outcomes of adults through residential relocation. More broadly, our findings suggest that efforts to integrate disadvant... Visit https://dataone.org/datasets/sha256%3Aa12b8c1f14eeabc92c1d91bd0311bc4aa3ddf6d7fb69ca798ca6926e7fa292c7 for complete metadata about this dataset.

Summary

The State of Early Education and Care in Boston: Supply, Demand, Affordability, and Quality, is the first in what is planned as a recurrent landscape survey of early childhood, preschool and childcare programs in every neighborhood of Boston. It focuses on potential supply, demand and gaps in child-care seats (availability, quality and affordability). This report’s estimates set a baseline understanding to help focus and track investments and policy changes for early childhood in the city.

This publication is a culmination of efforts by a diverse data committee representing providers, parents, funding agencies, policymakers, advocates, and researchers. The report includes data from several sources, such as American Community Survey, Massachusetts Department of Early Education and Care, Massachusetts Department of Elementary & Secondary Education, Boston Public Health Commission, City of Boston, among others. For detailed information on methodology, findings and recommendations, please access the full report here

The first dataset contains all Census data used in the publication. Data is presented by neighborhoods:

• Population 0 – 5 years;
• Population 0 – 2 years;
• Population 3 – 5 years;
• Race/ethnicity for children 0 – 4 years (White, non-Hispanic; Black; Asian; Hispanic/Latinx);
• Family type (married couples, female householder, male householder);
• Poverty status;
• Family median income in the past 12 months;
• Average cost of care as a percentage of median family income (infant, preschool);
• Share of families that cannot afford care (infant, preschool)

The Boston Planning & Development Agency Research Division analyzed 2013-2017 American Community Survey data to estimate numbers by ZIP-Code. The Boston Opportunity Agenda combined that data by the approximate neighborhoods and estimated cost of care and affordability.

Additional notes:

• Record Type: Each record represents a ZIP-Code defined neighborhood. See list below for detailed information on Boston ZIP-Codes used to create each one of the 15 neighborhoods.
• Data Quality: Numbers presented here came from 2013-2017 American Community Survey data. Therefore, these are ESTIMATES and have margin of errors. The smaller the geographical unit, the greater the margin of error. The Boston Planning & Development Agency analyzed the data to estimate numbers by ZIP-Code.
• Race/Ethnicity: Non-White Hispanics may be double counted due to data limitations.
• Cost of Care: The average cost of care as a percentage of median family income was computed assuming the annual average cost of infant care was $19,877 and the average cost of preschool care was $ 13,771 (Childcare Aware of America, 2019). For each neighborhood we estimated the impact of child care (infant and preschool) on its median annual family income.
• Affordability: The Department of Health and Human Services (DHHS) sets a standard regarding the affordability of child care, where the annual cost of child care should not exceed 10 percent of household annual income. Using this 10% threshold, we estimated that to afford market rate infant care, a family’s annual income would have to be at least $198,770. The census income bracket closest to this income was a family income of $150,000– 199,999. To afford preschool care, a family's annual income should be at least $137,710. Thus, the census income bracket that encompass this income is $125,000 - 149,999. For both infant and preschool care, we underestimated the number of families that can afford care.

The Moving to Opportunity (MTO) experiment offered randomly selected families housing vouchers to move from high-poverty housing projects to lower-poverty neighborhoods. We analyze MTO's impacts on children's long-term outcomes using tax data. We find that moving to a lower-poverty neighborhood when young (before age 13) increases college attendance and earnings and reduces single parenthood rates. Moving as an adolescent has slightly negative impacts, perhaps because of disruption effects. The decline in the gains from moving with the age when children move suggests that the duration of exposure to better environments during childhood is an important determinant of children's long-term outcomes. (JEL I31, I38, J13, R23, R38)

This dataset contains replication files for "The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects" and "The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates" by Raj Chetty and Nathaniel Hendren. For more information, see https://opportunityinsights.org/paper/neighborhoodsi/ and https://opportunityinsights.org/paper/neighborhoodsii/. A summary of the related publications follows. To what extent are children’s opportunities for upward economic mobility shaped by the neighborhoods in which they grow up? We study this question using data from de-identified tax records on more than five million children whose families moved across counties between 1996 and 2012. The study consists of two parts. In part one, we show that the area in which a child grows up has significant causal effects on her prospects for upward mobility. In part two, we present estimates of the causal effect of each county in the United States on a child’s chances of success. Using these results, we identify the properties of high- vs. low-opportunity areas to obtain insights into policies that can increase economic opportunity. The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service or the U.S. Treasury Department. This work is a component of a larger project examining the effects of tax expenditures on the budget deficit and economic activity. All results based on tax data in this paper are constructed using statistics originally reported in the SOI Working Paper “The Economic Impacts of Tax Expenditures: Evidence from Spatial Variationacross the U.S.,” approved under IRS contract TIRNO-12-P-00374.

Historical Dataset of Exc Children Have Opportunities School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Overall School District Rank Trends,Hispanic Student Percentage Comparison Over Years (2008-2020),Black Student Percentage Comparison Over Years (2008-2020),White Student Percentage Comparison Over Years (2008-2020),Two or More Races Student Percentage Comparison Over Years (2011-2020)

This dataset tracks annual total expenditure from 2010 to 2021 for Exc Children Have Opportunities School District

This dataset tracks annual diversity score from 2008 to 2020 for Exc Children Have Opportunities School District vs. Illinois

Population-Level Childhood Opportunity Index 3.0 Scores by Domain and Subdomains for Children with Phoenix Sepsis by Location of Pediatric Intensive Care Unit (PICU).

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show the number and percentages of opportunity to youth by Neighborhood Statistical Areas E02 and E06 in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

PopAges1619_e

# Population, ages 16-19, 2017

PopAges1619_m

# Population, ages 16-19, 2017 (MOE)

DisconYouth_e

# Disconnected youth: ages 16-19 not in school or in labor force, 2017

DisconYouth_m

# Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

pDisconYouth_e

% Disconnected youth: ages 16-19 not in school or in labor force, 2017

pDisconYouth_m

% Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

OwnChildInFam_e

# Own children in families, 2017

OwnChildInFam_m

# Own children in families, 2017 (MOE)

NoParentLabForce_e

# Own children in families with no parent in the labor force, 2017

NoParentLabForce_m

# Own children in families with no parent in the labor force, 2017 (MOE)

pNoParentLabForce_e

% Own children in families with no parent in the labor force, 2017

pNoParentLabForce_m

% Own children in families with no parent in the labor force, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

According to a survey conducted in 2024, Black Americans were found slightly less likely than white or Hispanic Americans to say that they think that children in their area have an opportunity to get a good education in the United States, with 75 percent of Black Americans sharing this belief. In comparison, 82 percent of white Americans and 77 percent of Hispanic Americans said that they thought children in their area have the opportunity to get a good education.

Abstract

Save the Children developed an early stimulation program that delivers actionable messages to mothers and other caregivers that show them how to interact and play with young children. The program also delivers a Child Development Card and two picture books, and instructions on how to use the card and the books to provide children with early learning opportunities. The program is low cost and potentially scalable because it builds on an existing delivery platform, and trains current community health care providers to deliver additional messages on early childhood stimulation practices.

Geographic coverage

Bangladesh is divided into seven major administrative regions called divisions, and the study takes place in three of Bangladesh’s seven divisions: Barisal (a southern district), Chittagong (a district in the southeast), and Sylhet (a district in the northeast). Within these three divisions, the study is located in three districts: Barisal (in the division of Barisal), Chittagong (in the division of Chittagong) and Moulvibazar (in the division of Sylhet). Districts are subdivided into subdistricts, or upazilas. Within these three districts, the study is located in three upazilas: Muladi (in the district of Barisal), Satkania (in the district of Chittagong), and Kalaura (in the district of Moulvibazar). Upazilas are subdivided into unions, and the study takes place in 30 unions: 4 unions in Muladi, 16 unions in Satkania, and 10 unions in Kalaura.

Analysis unit

Households Individuals

Universe

Households with children between 3 and 18 months of age residing in the catchment area of participating community clinics at the time of baseline data collection.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling of Households The study sample frame was generated from community clinic health assistant records, which had the advantage of being the centralized government document of record containing the population frame for all households with children under five years of age. The health assistant dataset included data for all three upazilas of interest. Of a total of 41 unions located in the three upazilas, 11 unions were excluded from the sampling frame. Six of these had incomplete data, and five were excluded because they had only one community clinic and the study design required each union to have at least two clinics. The final sample included 78 community clinics, located in 30 unions.

Within the selected unions and community clinics, eligible households included those with children aged between 3 and 18 months who resided in selected community clinics' catchment areas during the baseline data collection period (November 2013-January 2014).We randomly sampled 33 households from each community clinic's catchment area to participate in the study. The sample was restricted to households with children aged three months or older because the main developmental assessment tool chosen for the evaluation (the Bayley-III; Bayley, 2006) had not been previously validated on children under the age of three months in Bangladesh. Furthermore, because the Bayley-III test is only valid for children up to the age of 42 months, we restricted the upper age limit of participating children to 18 months or younger at the time of baseline data collection in order to collect valid endline data 24 months later.

Replacement The community clinic health assistant records were not up to date, so the team developed rules for replacing households that were found to be ineligible or "out-of-scope," as well as households that refused to participate. We randomly selected 20 additional replacement households from within each community clinic and included them in a separate list, with each household randomly sorted from 1 to 20. If one of the 33 households originally selected was found to be ineligible or refused to participate, the field interviewer replaced it with the first household from the 20-household replacement list, and continued replacing households in order thereafter.

Overall, the majority of replacements were required because households were identified as ineligible, and only a few replacements were needed for households that refused to participate in the study (N = 39, or 1.5 percent of the sample). Households were ineligible if they did not fit the target sample description: "Households with children from 3-18 months of age that live in the selected community clinics' catchment areas during the period of the baseline data collection." This included: (a) households that had permanently left the catchment area (N = 300); (b) households with incorrect location information in the birth records (N = 291); (c) households with children who were ineligible due to inaccurate birth dates (N =173); and (d) households that were temporarily absent from the catchment area (N =159). For all 39 cases of refusal, the data collectors completed a non-complier questionnaire that captured some basic characteristics of this group to compare with the compliers.

Mode of data collection

Face-to-face [f2f]

Research instrument

Instruments AIR, ICDDR,B, and Data International Ltd. worked with Save the Children, the World Bank, and the evaluation advisory board to develop the study instruments. The team developed the data collection instruments by drawing from existing national and international tools aligned with the evaluation's outcomes of interest. The core indicators included child development outcomes, anthropometric measures, and parenting stimulation questions, although the final instrument contained many more relevant indicators. Where possible, indicators were measured using questions and approaches that had already been field tested in Bangladesh to ensure that they were appropriate for the local context and the target populations. We also designed the instruments to be of a manageable length in order to avoid interviewer or respondent fatigue and ensure high-quality data. On average, the final survey instruments took 30 minutes to complete.

Endline data collection tools resembled the instruments used at baseline. As discussed above, some instruments were modified slightly based on lessons learned during baseline data collection and monitoring data collection. The non-compliance survey was not administered at endline. Two new measures were added during endline: the Wolke Behavioral Rating Scale, which measures children's behavior during the Bayley-III; and a focus group protocol, with fathers and mothers grouped separately.

This dataset tracks annual average revenue per student from 2008 to 2020 for Exc Children Have Opportunities School District

BackgroundChildren from families with low socioeconomic status (SES), as determined by income, experience several negative outcomes, such as higher rates of newborn mortality and behavioral issues. Moreover, associations between DNA methylation and low income or poverty status are evident beginning at birth, suggesting prenatal influences on offspring development. Recent evidence suggests neighborhood opportunities may protect against some of the health consequences of living in low income households. The goal of this study was to assess whether neighborhood opportunities moderate associations between household income (HI) and neonate developmental maturity as measured with DNA methylation.MethodsUmbilical cord blood DNA methylation data was available in 198 mother-neonate pairs from the larger CANDLE cohort. Gestational age acceleration was calculated using an epigenetic clock designed for neonates. Prenatal HI and neighborhood opportunities measured with the Childhood Opportunity Index (COI) were regressed on gestational age acceleration controlling for sex, race, and cellular composition.ResultsHigher HI was associated with higher gestational age acceleration (B = .145, t = 4.969, p = 1.56x10-6, 95% CI [.087, .202]). Contrary to expectation, an interaction emerged showing higher neighborhood educational opportunity was associated with lower gestational age acceleration at birth for neonates with mothers living in moderate to high HI (B = -.048, t = -2.08, p = .03, 95% CI [-.092, -.002]). Female neonates showed higher gestational age acceleration at birth compared to males. However, within males, being born into neighborhoods with higher social and economic opportunity was associated with higher gestational age acceleration.ConclusionPrenatal HI and neighborhood qualities may affect gestational age acceleration at birth. Therefore, policy makers should consider neighborhood qualities as one opportunity to mitigate prenatal developmental effects of HI.

About 1 in 10 children need extra help developing speech and language skills. Without help, it may be harder for these children to listen, talk, read and play with others.

Preschool Speech and Language Program staff can teach you how to help your child develop their communications skills, giving them the best opportunity for healthy development.

This dataset tracks annual math proficiency from 2018 to 2021 for Exc Children Have Opportunities School District vs. Illinois

This dataset tracks annual hispanic student percentage from 2008 to 2020 for Exc Children Have Opportunities School District vs. Illinois