Methodology

The NHVRC team collects two main types of data to create the yearbook:

• Service data, which include information about families served by home visiting, agencies that provide home visiting services, and those who work with families
• Population data, which include information about the population of families who could benefit from home visiting

The team combines these data to create aggregate data products that describe—

• Home visiting nationally and in each state through evidence-based model data, and who could potentially benefit from home visiting through American Community Survey (ACS) data
• The geographic reach of evidence-based home visiting
• The federal contribution to home visiting through MIECHV and Tribal MIECHV administrative data
• Home visiting provided nationally by emerging models

Service Data Sources

The team collected data from various sources to capture comprehensive information about home visiting at the local, state, and national levels. The team received data from—

• Evidence-based models, which include models operating in the United States in 2023 that met Home Visiting Evidence of Effectiveness (HomVEE) project criteria for evidence of effectiveness at that time (17 of 18). (Source: Models represented in the evidence-based numbers include Attachment and Biobehavioral Catch-Up (ABC), Child First, Early Head Start Home-Based Option (EHS), Family Check-Up (FCU), Family Connects, Family Spirit, Health Access Nurturing Development Services (HANDS), Healthy Families America (HFA), Home Instruction for Parents of Preschool Youngsters (HIPPY), Maternal Early Childhood Sustained Home-Visiting (MECSH), Maternal Infant Health Outreach Worker Program (MIHOW), Maternal Infant Health Program (MIHP), Nurse-Family Partnership (NFP), Parents as Teachers (PAT), Play and Learning Strategies (PALS), Promoting First Relationships (PFR), and SafeCare Augmented. Another model, Preparing for Life — Home Visiting, was approved by HomVEE in 2023. Preparing for Life — Home Visiting is not included in the 2023 data collection because it did not start serving families in the United States until 2024.)^{Go to footnote #>1}
• Emerging models, which include models that have not yet met HomVEE evidence criteria but do meet other evidence standards (11 of 13).  See the methodology section of the 2021 Home Visiting Yearbook, which includes the most recent description of how emerging models are selected, for more information.(Source: Models represented in the emerging models numbers include Arizona Health Start Program, AVANCE Parent Child Education Program (PCEP), Baby TALK, Early Steps to School Success (ESSS), First Born and More, Following Baby Back Home (FBBH), High Risk Perinatal Program, Parent-Child Assistance Program (PCAP), ParentChild+, Team for Infants Exposed to Substance use (TIES) Program, and Welcome Baby.)^{Go to footnote #>2}
• State and territory MIECHV agencies (48 of 56).
• National Tribal MIECHV program (1 of 1).

Evidence-Based Model Data

We contacted each of the 18 home visiting models operating in the United States in 2023 that met HomVEE criteria for evidence of effectiveness at that time.

The team invited each model to share data on the characteristics of participants served in 2023 and information about local agencies that served them. We provided models with a list of requested variables and guidance on reporting that aligns with federal and MIECHV variable guidance. However, we accepted data from models in whatever format they had available.

The full data request included the following variables:

Local agency characteristics

• Agency names and addresses
• Geographic service areas
• Total number of full-time equivalent (FTE) home visitors implementing the model at the end of the reporting year
• Total number of FTE supervisors implementing the model at the end of the reporting year
• Services provided by Indigenous-led organizations (Source: We asked evidence-based models to identify programs led by Indigenous organizations, regardless of funding source.)^{Go to footnote #>3}

Participant characteristics

• Total number of children served
• Total number of families/households served
• Total number of home visits completed
• Total number of virtual home visits completed
• Caregiver ethnicity
• Caregiver race
• Caregiver educational attainment
• Child age
• Caregiver age
• Child insurance status
• Primary language
• Household income(Source: This may reflect annual income, Medicaid eligibility, percentage of families below federal poverty level, or model definition of low income.)^{Go to footnote #>4}

Not all evidence-based models were able to provide data for each variable, but we accepted the data that these models had available. The following tables indicate which variables were provided by the 17 evidence-based models operating in the United States that shared some data for the 2024 Yearbook.

Sixteen evidence-based models provided data on local agency characteristics.

Local Agency Characteristics: Data Provided by Evidence-Based Models

Seventeen evidence-based models provided service delivery data.

Service Delivery: Data Provided by Evidence-Based Models

Fourteen evidence-based models provided data on participant characteristics.

Participant Characteristics: Data Provided by Evidence-Based Models

Nine evidence-based models provided data on services provided by Indigenous-led organizations.

Services Provided by Indigenous-Led Organizations: Data Provided by Evidence-Based Models

Emerging Model Data

For the 2024 Yearbook, we contacted 13 emerging models asking them to provide information on their model, service delivery information, and if available, participant demographics. The team invited each model to share data on the characteristics of participants served in 2023.(Source: See the methodology section of the 2021 Home Visiting Yearbook (https://nhvrc.org/yearbook/2021-yearbook/methodology/), which includes the most recent description of how emerging models are selected.)^{Go to footnote #>5}

The full data request included the following variables:

Local agency characteristics

• Geographic service areas
• Number of local agencies
• Total number of full-time equivalent (FTE) home visitors implementing the model at the end of the reporting year
• Total number of FTE supervisors implementing the model at the end of the reporting year

Participant characteristics

• Total number of children served
• Total number of families/households served
• Total number of home visits completed
• Total number of virtual home visits completed
• Caregiver ethnicity
• Caregiver race
• Caregiver educational attainment
• Child age
• Caregiver age
• Child insurance status
• Primary language
• Household income(Source: This may reflect annual income, Medicaid eligibility, percentage of families below federal poverty level, or model definition of low income.)^{Go to footnote #>6}

Not all emerging models were able to provide data for each variable, but we accepted the data that these models had available. The following tables indicate which variables were provided by the 11 emerging models operating in the United States that shared some data for the 2024 Yearbook.

Eleven emerging models provided data on local agency characteristics.

Local Agency Characteristics: Data Provided by Emerging Models

Eleven emerging models provided service delivery data.

Service Delivery: Data Provided by Emerging Models

Eleven emerging models provided data on participant characteristics.

Participant Characteristics: Data Provided by Emerging Models

MIECHV Service Data

MIECHV legislation requires awardees to report data yearly to the federal government. These data include information such as the number of home visits conducted, number of participants served, and participant demographics. The team asked MIECHV agencies in each state to share a copy of this administrative data report. Most were able to share data, but a few were not. We received data from 48 out of 56 state agencies, which we used to produce the MIECHV Data Tables. (The Administration for Children and Families provided aggregate data for Tribal MIECHV grantees, which we used to produce the Tribal MIECHV Data Table.)

The Health Resources and Services Administration (HRSA) provides states and territories with guidelines for defining demographic variables.

The number of home visits (in-person and virtual), families served, children served, and participant demographics were obtained from Form 1 (Source: Table names reflect HRSA guidance for fiscal year 2023, the year reported in this yearbook. )^{Go to footnote #>7} for the reporting period 10/01/2022 through 9/30/2023, using tables 1, 2, 4–7, 9, 12, 13, 15, 17, and 18 (listed below).

• Table 1: Unduplicated Count Of New And Continuing Program Participants Served By MIECHV
• Table 2: Unduplicated Count Of Households Served By MIECHV
• Table 4: Adult Participants By Age
• Table 5: Index Children By Age
• Table 6: Participants By Ethnicity
• Table 7: Participants By Race
• Table 9: Adult Participants By Education Attainment
• Table 12: Primary Language Spoken At Home
• Table 13: Household Income In Relation To Federal Poverty Guidelines
• Table 15: Home Visits
• Table 17: Unduplicated Count Of Households By Evidence-Based Home Visiting Model Or Promising Approach
• Table 18: Participants By Type Of Health Insurance Coverage

The numbers of home visitors and supervisors were obtained from Form 4 for the reporting period 7/01/2023 through 9/30/2023, using table Table A.4: Staff Recruitment and Retention.

Service Data Analysis

We conducted a rigorous data cleaning and analysis procedure for all data sources. For the evidence-based model data, we reviewed each model dataset to determine which data elements were available among those in our initial data request. We then examined all models to determine how to combine and report data uniformly across models for state and national profiles. We then cleaned the data to ensure all reported elements were complete. Next, we combined data across models using statistical analysis software.

NHVRC data teams conducted quality assurance checks to verify the data and prepared draft profiles. The team sent draft profiles to model and state staff for additional review. When models provided updated data or revisions to the data in profiles, we updated and re-ran analyses as indicated by model feedback. The NHVRC data team then conducted a final quality assurance check of the data before updating yearbook text and products (e.g., state and national profiles).

The NHVRC team used a similar process for emerging model data. We reviewed each model dataset to determine which data elements were available, then examined all models to determine how to combine and report data uniformly. We then cleaned the data to ensure all reported elements were complete. Next, we combined data across models using statistical analysis software, conducted quality assurance checks, and provided data back to models to review for accuracy. We incorporated any revisions into a final dataset, which was then checked for quality again, before updating emerging model yearbook elements.

Most states provided NHVRC with MIECHV administrative data in PDF format. NHVRC staff double-entered state MIECHV administrative data to ensure accuracy before the software analysis. We cleaned data and combined data using statistical analyses software. The team then conducted quality assurance checks and created draft MEICHV data tables. The team then sent draft data tables to states for their review. After states reviewed their data, we made necessary updates to the data, re-ran analyses, and conducted a final quality assurance check before updating yearbook elements.

Confidentiality and Cell Suppression

The NHVRC uses standards for minimum cell sizes to protect the confidentiality of home visiting participants by avoiding the release of information that can be used to identify individuals. To maintain the confidentiality of model and state data, we conducted cell suppression of race and ethnicity variable categories with 10 or fewer participants. These counts are replaced with an * in the data. A value of zero does not violate the minimum cell size policy.

Treatment of Missing Data

Missing and unknown data were not included in calculations of percentages of demographic data in the national, state, and model profiles. Missing data were excluded in calculations of percentages of the following variables:

• Caregiver ethnicity
• Caregiver race
• Caregiver educational attainment
• Child age
• Caregiver age
• Child insurance status
• Primary language
• Household income

For MIECHV Data Tables, we also excluded Unknown/Did Not Report data in calculations of percentages of the following variables:

• Caregiver age
• Child age
• Primary language
• Child insurance status
• Caregiver ethnicity
• Household income
• Caregiver race
• Caregiver education

National Aggregated Analyses

Following cleaning and analysis, we aggregated data to the extent possible to describe who is being served in various products, such as the National Profile and national maps. It was not always possible to aggregate all categories. For example, a model may collect insurance status as yes or no rather than the three categories we report of public, private, and uninsured. When we could not include model data in aggregated products, we included it in individual Model Profiles.

Evidence-based models

Although models do not uniformly report data, the NVHRC team combined as much of the evidence-based model service data as possible to include in the National and State Profiles. Data that could not be combined and included in the National Profile were included in individual Model Profiles. The following tables indicate which variables provided by evidence-based models were used in the National Profile.

Seventeen evidence-based models provided service delivery data that were used in the National Profile.

Service Delivery: Reported in National Profile

Fourteen evidence-based models provided data on participant characteristics that were used in the National Profile.

Participant Characteristics: Reported in National Profile

Emerging models

We also aggregated service data provided by emerging models to create a national summary describing families served through emerging home visiting and provide information on who provides services. The following tables represent which models provided variables that could be aggregated in the emerging models summary.

Eleven emerging models provided service delivery data that were used in the emerging models summary.

Service Delivery: Reported in National Summary

Eleven emerging models provided data on participant characteristics that were used in the emerging models summary.

Participant Characteristics: Reported in National Summary

National map analyses

We produced two maps as part of this analysis, one to highlight counties where evidence-based home visiting programs are located, and the other to assess the reach of service areas in each state and territory during 2023.

1. Counties With at Least One Local Agency Delivering Evidence-Based Home Visiting (2023). In this analysis, we identified all counties in the United States and its territories where there was at least one local agency using an evidence-based model in 2023. We estimated county-level coverage using data from the Administration for Children and Families on behalf of Tribal MIECHV grantees and 16 evidence-based models on their program locations across the United States and its territories.
2. Percentage of Zip Codes in Which Families Receive Evidence-Based Home Visiting by State (2023). We estimated the percentage of zip codes served by evidence-based home visiting programs using data collected from 12 evidence-based models and from MIECHV Form 4 data. Our coverage estimates may be low because not all models reported service areas. On the other hand, the coverage estimates may be high because some models reported service area by county rather than zip code, and we made the simplifying assumption that models reporting county-level service areas served all zip codes in each reported county. Also note that we used population estimates from the 2023 ACS to exclude zip codes with no population from the analysis to make cross-state comparisons more meaningful.

Population Data – American Community Survey Source

The 2024 Home Visiting Yearbook catalogs national- and state-level information on potential beneficiaries of home visiting using information from the ACS. We first define potential beneficiaries broadly. We then examine subgroups of families who might be a higher priority for services based on several priority criteria. ACS data were analyzed for all 50 states and the District of Columbia, but not for territories or individual Indigenous communities.

The team relied on the 2023 ACS 5-year (2019–2023) file accessed through the Integrated Public Use Microdata Series (IPUMS). The ACS is a nationwide, ongoing survey designed to provide data on demographic, housing, social, and economic issues. IPUMS grants access to ACS microdata, where each record represents a person.

Potential Beneficiaries of Services

We define potential beneficiaries of home visiting services as families and subfamilies with pregnant women and/or children under 6. (Subfamilies are families that live in the household of someone else.) First, we estimate the number of families and subfamilies with children younger than 6 years old who are not yet enrolled in school (that is, not in kindergarten or a higher grade). To this estimate, we add an estimate of the number of families and subfamilies that include a pregnant woman and are not otherwise counted.

Estimates of pregnant women are based on adjusted counts of families with infants because the ACS does not identify pregnancy status. Specifically, we count the number of families with infants but no other children under age 7 in first grade or higher, as a proxy estimate of pregnant women without a child under age 6 not yet enrolled in kindergarten (assuming rough stability in the number of births from one year to the next). We multiply the number of families with infants by 0.75 to account for 9-month pregnancy. (Source: We do not attempt to refine the estimate to account for (1) fetal and infant deaths or (2) the lag in time before a woman’s pregnancy would be verified; the first adjustment would raise the estimate of pregnant women not already counted, while the second would lower it.)^{Go to footnote #>8}

Families With High Priority for Services

To identify a subpopulation of “high-priority families,” we count the number of families with young children and pregnant women who meet 1 of 5 different economic and demographic criteria (as defined below) and the number of families that meet at least 2 such criteria. We conferred with the Advisory Committee to select our priority criteria. Although other criteria could also be considered, we chose these because they align with several of the priority areas from the MIECHV legislation, they align with several of the model requirements for enrollment, and they are available in the ACS.

Priority Criteria

We estimate the number of families with preschool children under 6 and pregnant women who meet each of the following criteria at the national and state levels:

• Presence of an infant; that is, a child younger than 1 year old. By definition, none of the pregnant women without children under 6 meet this criterion.
• Low income, where family income is below 100 percent of the federal poverty threshold.
• Young mother or young pregnant woman. We define young as under 21 years old.
• Single mother, never married.
• Low parental education. We count the number of families in which the child’s parent(s) have not completed 12th grade. (Source: In two-parent households, we consider both parents’ educational levels; in one-parent households, we consider only that parent’s educational attainment. For pregnant women, we look at the education of the mother only.)^{Go to footnote #>9}

Aggregated Data Products

Although models do not uniformly report data, the NVHRC team combined as much of the evidence-based model data we received as possible to include in the National Profile. Data that could not be combined and included in the National Profile were included in individual Model Profiles. These data represent the most comprehensive summary of home visiting services provided by evidence-based home visiting models across the nation. We aggregated data across models and then used the summarized data to create the—

• NHVRC National Profile featuring model data on service numbers and participant demographics
• NHVRC State Profiles featuring model data on service numbers and participant demographics by state and ACS data on potential beneficiaries by state
• NHVRC Model Profiles featuring model data on service numbers, participant demographics, survey information on model requirements, and geographic information on where models operate
• Two national maps featuring data from evidence-based models and the Administration for Children and Families on behalf of Tribal MIECHV grantees on local agency locations; and evidence-based model data combined with MIECHV data on participant geographic service areas

We aggregated data across emerging models and then used the summarized data to create the—

• Who is being served By Emerging Models section featuring model data on service numbers and participant demographics
• NHVRC Model Profiles featuring model data on service numbers, participant demographics, survey information on model requirements, and geographic information on where models operate