Matern Child Health J DOI 10.1007/s10995-015-1727-9

A Nationally Representative Study of Early Childhood Home Visiting Service Use in the United States Paul Lanier • Kathryn Maguire-Jack Hannah Welch

•

Ó Springer Science+Business Media New York 2015

Abstract Early childhood home visiting (HV) services are expanding broadly across the United States. Supported by federal policy, HV is now an integral part of maternal and child health services. However, no nationally representative estimate of HV use is available and no research has compared HV use across states. The 2011/12 National Survey on Children’s Health was used to estimate the national and state prevalence of HV use for children 0–3 years. Generalized linear mixed modeling was used to predict HV use. An estimated 2,137,044 US children and families received HV during pregnancy and up to child age of 3 years. State HV prevalence range was 3.7–30.6 %. Nationally, 19.1 % of children below the federal poverty line received HV services. Although family poverty increased the odds of receiving HV services, higher rates of child poverty at the state level predicted less use of HV services. Important predictors of HV use include infant/ child need factors (health risk, adverse experiences), predisposing factors (family size), and enabling factors (insurance type). This study provides the first estimates of national and state HV service use. Although findings indicate HV services are targeted to children at elevated risk for poor physical or developmental outcomes, our estimates show the vast majority of at-risk children did not receive HV services, including more than 80 % of lowincome children, 76 % of preterm infants, and 57 % of very low birth weight infants. Increasing HV service P. Lanier (&)
H. Welch UNC-Chapel Hill School of Social Work, 325 Pittsboro Street, Chapel Hill, NC 27599, USA e-mail: [email protected] K. Maguire-Jack The Ohio State University College of Social Work, 1947 College Road, Columbus, OH 43035, USA

availability could decrease negative health outcomes for young children. Keywords Home visiting
Prevention
Policy
Public health

Introduction Home visiting (HV) has been used in a variety of forms for over a century to promote the health of pregnant women and young children [1, 2]. HV is a delivery strategy that encompasses a diverse array of services [3]. HV services can range from a one-time visit from a public health worker (as follow-up on a birth) or a teacher (as part of a preschool protocol) to multi-week or multi-year in-home visits from nurses and social workers who deliver programs intended to improve a host of child and family outcomes. The shared belief of all HV strategies is that entering the family’s home environment has significant benefits to effective service delivery [3]. For example, HV reduces barriers to receiving help, enables the visitor to observe and access additional information to promote family-centered interventions, and perhaps most important, the visitor is more likely to develop and strengthen the helping relationship [3, 4]. Randomized controlled studies of early childhood HV programs indicate HV can provide a significant return on public investment by improving infant health outcomes and preventing child maltreatment [5]. Empirical and political support for HV culminated in 2010 in the federal Maternal, Infant, and Early Child Home Visiting (MIECHV) program [6]. Expansion of HV in the US is underway [2, 7]. A significant gap in the HV knowledge base stems from the lack of accurate estimates of HV use (i.e., number of

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families receiving services), indicators of which families are most likely to receive HV, and unanswered questions regarding the efficacy of the various HV programs. For example, of 40 HV models evaluated for effectiveness, only 17 met federal criteria as evidence-based practices [3]. Further, an unknown number of community-based HV programs are in operation but have not been evaluated. Although individual programs might produce reliable estimates of the number of families served, nationwide estimates of HV use are not known, and thus, the public health impact of HV is unknown. Using service estimates from a select group of HV programs, researchers estimated 200,000 children and families were enrolled in preventative HV services in 1993 [8], increasing to 400,000 families by 2005 [9]. However, a current and more precise estimate that reflects the diversity of early childhood HV programs is needed to benchmark the reach of services nationally. Ongoing debate on HV centers on whether these services should be universal or targeted as a prevention service. In many countries, HV services are provided universally as part of broad policy promoting maternal and child health. In contrast, HV services in the US are selective, and with rare exceptions [7], are targeted to those at highest risk for poor outcomes [10]. This model of selective HV services has continued even though nearly all research supporting HV has been generated from programs developed and tested in the US, and despite a 20 year-old recommendation from the US Advisory Board on Child Abuse and Neglect endorsing universal access to HV services [11]. Given this selective use of HV, policies and programs have varying criteria for service eligibility. Federal MIECHV funding supports pregnant women and parents of children 0–5 years. To be eligible for these funds, an HV program must be voluntary and evidence-based; however, each state chooses its model(s) and geographic area(s) for service expansion based on a statewide needs assessment. Thus, models vary substantially regarding the target population [12]. As a voluntary health service driven by individual and contextual determinants, this study aimed to assess the extent to which HV use is associated with selected predisposing factors (risk factors), enabling factors, and need [13]. Present Study This study sought to estimate national- and state-level HV service use in the United States using a nationally representative survey. Federal funds for HV services in each state come through a variety of formula grants (e.g., MIECHV) to provide a proportional baseline level of federal funding. However, states and communities also fund HV services at different levels through a patchwork of

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public and private sources. Therefore, establishing the variation of state-level prevalence of HV use provides one indicator of the level of public investment in early childhood HV. Study Hypotheses and Research Questions We hypothesized the demand for HV is influenced by individual factors such as socioeconomic status (SES) and infant or maternal health. However, we also expected voluntary HV use to be driven by the supply (i.e., availability/accessibility) of HV programs in the community. Funding and support for HV is expected to vary between states, and state-level predictors are expected to impact individuals’ HV use. We use multilevel modeling to account for child clustering within states. The following research questions guided this study: 1. 2. 3.

What is the prevalence of HV use in the United States among children 0–3 years? What variation exists in HV use across states? Which individual- and state-level variables predict HV use?

Methods This study was a secondary data analysis of the 2011/12 National Survey of Children Health [14] (NSCH). The protocol for this investigation was reviewed by the University of North Carolina at Chapel Hill Office of Human Research Ethics. Data and Measures Survey Data The NSCH was conducted by the National Center for Health Statistics (a CDC center). The NSCH goal was to assess the physical and emotional health of children ages 0–17 years and to identify factors affecting child well-being. One child from each household was randomly chosen as the survey subject. The NSCH is a cross-sectional telephone survey using random-digital-dial for both landline (list-assisted) and cell-phone (independent) numbers. Surveys were administered from July 2011 through January 2012. Sampling weights were used to adjust for non-response and unequal selection bias, and to produce national and state-specific results representative of all non-institutionalized children. The NSCH screened 847,881 households for eligibility, and 187,422 households reported age-eligible children in the home and were invited to complete the survey. Of

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these, 95,677 households completed the NSCH in 1 of 6 languages in which the survey was offered. From this sample, we identified a subsample of 19,450 households with very young children (birth–3 years). We excluded 49 surveys that were missing responses to the question about HV service use, yielding a final study sample of 19,401 children. Factors Affecting HV Use NSCH data included individual and contextual predisposing and enabling factors. We also considered demographic and SES factors potentially related to HV service use. Given that the data were cross-sectional, we focused on time-invariant variables and factors likely to exist prior to HV service use (i.e., variables not likely to be an outcome of HV service use). These study factors included child gender, age, race (non-Hispanic white, non-Hispanic black, non-Hispanic multi-racial/other, and Hispanic); child’s family structure (two parent-biological or adopted, two parent-step family, single mother-no father present, and other); household income relative to the federal poverty level (FPL; \ 100 % FPL, 100–199 % FPL, 200–399 % FPL, and 400 % or more FPL); family size (only child, 2, 3, and 4 or more total children); respondent relationship (mother, father, or other); primary language spoken in the home (English or other); and insurance type (public, private, and uninsured). Mother’s age at birth of study child (B20 years and C21 years) was derived using the current ages of the mother and the subject child. We used the statelevel child poverty rate for children birth to 5 years from the US Census Bureau’s 2010–2012 American Community Survey data [15]. Based on the un-weighted mean state poverty rate, state-level poverty was recoded as a dichotomous variable of either low (\25 %) or high (C25 %).

poor mental or physical outcomes. These risk-screening strategies often include an assessment of cumulative psychosocial and environmental risk. The NSCH includes a measure of nine adverse childhood experiences (ACEs): (1) socioeconomic hardship, (2) divorce/separation of parent, (3) death of parent, (4) parent served time in jail, (5) witness to domestic violence, (6) victim of neighborhood violence, (7) lived with someone who was mentally ill or suicidal, (8) lived with someone with alcohol/drug problem, and (9) treated or judged unfairly due to race/ethnicity. We compared children with 0, 1, or C2 ACEs as an indicator of need/risk. Home Visiting Use If the subject child was 0–3 years, the NSCH survey asked the following question: Some new parents are helped by programs that send nurses, health care workers, social workers, or other professionals to their home to help prepare for the new baby or take care of the baby or mother. Between the time [his/her mother was] pregnant with (child’s name) and up until the present day, did someone from such a program visit your home? Notably, the question specifies professional home visitors. Some evidence-based HV models employ only individuals with professional degrees whereas other programs use paraprofessionals or a combined team approach [3, 5]. It is unknown whether parents made this distinction, but it is unlikely that most parents would differentiate between a professional and paraprofessional home visitor in terms of service use. A follow-up question determined if the home visitor discussed any of seven HV topics: child safety, maternal well-being, healthcare access, bonding with child, smoking/alcohol use in home, promoting child develop, and other needed services (see Table 1).

Need Factors Analysis Because HV is often prescribed in response to a perceived risk to infant health, we explored child-level variables related to an evaluated infant need. For markers of infant health status, we included variables for child birth weight (normal, low birth weight [1,500–2,500 g], very low birth weight [\1,500 g]) and gestation status (not premature or premature [more than 3 weeks before due date]). The telephone survey included a five-item screener to identify children with special health care needs: (1) use of medicine prescribed by a doctor; (2) elevated service use or need; (3) functional limitations; (4) use of special therapies; or (5) ongoing emotional, developmental, or behavioral conditions. Typically, families are referred to HV services when screening indicates the parent and/or child is at risk for

Bivariate Analysis To account for the complex survey design, the weighted proportion of HV use nationally and for each state was estimated using SAS Survey Procedures [16]. All variables were categorical, thus Chi square tests were used for all bivariate analyses. The weighted percentage of children ages 0–3 years who received HV for each state was calculated and the results were entered into ArcGIS 10.1 to develop a distributional map [17]. For the purposes of our analysis, we included the District of Columbia with the 50 states, and divided the states into three groups of 17 states categorized by level of HV use (low, medium, or high).

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Matern Child Health J Table 1 National estimate of early childhood home visiting and topics discussed during home visit # Receiving services

% Children 0–3 years

SE of %

2,137,044

13.6

0.50

Making sure child is safe and does not get hurt

1,644,408

10.5

0.46

How to get healthcare for child

1,509,501

9.7

0.43

Building a close relationship with child

1,455,491

9.3

0.43

Maternal well-being

1,455,562

9.3

0.44

Smoking or alcohol use in home

1,365,477

8.8

0.41

Using toys/play to help child learn, grow & develop

1,355,781

8.7

0.41

994,633

6.4

0.36

Family participated in a home visitation program Topics discussed:

Other services that might help the family

Multilevel Modeling We estimated a generalized linear mixed model with households clustered within all 50 states and the District of Columbia. First, a null model was estimated to calculate the intraclass correlation coefficient (ICC). The ICC measures the proportion of the variance in HV service use accounted for by the family’s state of residence. For ICC calculation, we assume level-1 residuals follow the logistic fixed variance distribution (p2/3). Second, a multivariate model was fit using all covariates. The model included a random intercept term, meaning that each child’s probability of receiving HV is estimated, in part, as the linear probability of HV for that child’s state. The slopes for the individual-level predictors were estimated as fixed effects, meaning the slope for a given variable was expected to be equal across all states. The model was estimated using SAS/GLIMMIX, which allows for weighting with a generalized linear mixed model using a binary outcome. Standard errors of the parameter estimates were calculated using a covariance sandwich estimator. Odds ratios (ORs) and 95 % confidence intervals (CIs) for each variable are reported. CIs that do not span 1 are statistically significant at p \ .05.

Results The weighted estimate of the number of US families who participated in a HV program between pregnancy and child’s age of 3 years was 2,137,044 families (13.6 % of the population 0–3 years). Table 1 provides the national estimate of the number and proportion of children 0–3 years that received HV and the topics discussed during the HV calls. Topics discussed most frequently included ensuring the child’s safety (10.5 %) and access to health care for the child (9.7 %). Among families receiving HV

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services, HV worker and the parents discussed a mean total of 4.52 of the seven identified topics (95 % CI 4.35, 4.68) and 25.3 % of families (SE = 1.70) reported the HV worker discussed all seven topics with the parents. The state-level prevalence rates of HV use ranged from 3.7 to 30.6 % (Table 2). Based on the null multilevel model, the ICC was 9.6 %, indicating a substantial portion of the variance in individual-level HV use is accounted for by the family’s state of residence. Figure 1 provides a US map depicting rates of HV per child population. The regional geographic trend shown in this map suggests a lower rate of HV use in the Southeastern and Southwestern regions, and higher rate of HV use in Midwestern, MidAtlantic, and Northeastern states. Table 3 provides descriptive information comparing children by receipt of HV services. Children most likely to receive HV services included the following groups: those whose families had household incomes less than 100 % FPL; those receiving public health insurance; and those born prematurely or with low/very low birth weight. In addition, children with a special health care need were more likely to receive HV services than children without such needs. As compared with mothers and other caregivers included in the NSCH survey, fathers were 29 % less likely to report HV services. Children with histories of more than two ACEs were also more likely to receive HV than children who had experienced 0–1 ACEs. Finally, families living in a state with a poverty rate of less than 25 % were more likely to receive HV services than families living in a state with a poverty rate of 25 % or more. Results of the generalized linear mixed model are shown in Table 4. These results demonstrate, when controlling for other factors, children who tend to have greater needs are more likely to receive HV services. Specifically, the odds of a child with special health care needs receiving HV services are 1.93 times greater than a child without such

Matern Child Health J Table 2 Home visiting service population size and rate of service use for very young children (0–3 Years) by state

State

Alaska

Weighted frequency of children who received a home visit

SD

% Very young children (0–3 yrs.) who received a home visit

SE

5,377

1,036

12.2

2.2

Alabama

15,060

4,278

6.4

1.8

Arkansas

15,258

3,765

9.6

2.2

Arizona

31,632

7,273

9.0

2.0

California

211,034

47,780

10.7

2.3

Colorado

40,714

7,176

15.2

2.5

Connecticut Washington, DC

18,925 3,008

3,533 781

12.9 10.7

2.3 2.6

Delaware

8,489

1,460

20.2

3.1

Florida

111,451

23,494

13.1

2.6

Georgia

27,558

8,695

5.2

1.6

Hawaii Iowa Idaho

6,634

1,545

10.3

2.3

27,904

3,882

18.3

2.4

4,786

1,228

4.7

1.2

Illinois

131,912

22,423

20.5

3.1

Indiana

73,321

12,253

22.0

3.2

Kansas

25,918

4,740

16.5

2.7

Kentucky

39,724

6,755

17.8

2.7

Louisiana

24,668

5,452

10.3

2.2

Massachusetts

82,278

10,700

29.0

3.2

Maryland

35,565

8,233

12.6

2.7

Maine Michigan

14,726 99,005

1,930 17,635

28.1 20.9

3.2 3.3

Minnesota

86,905

10,896

30.6

3.2

Missouri

59,681

9,846

19.8

2.9

Mississippi

15,227

4,092

10.2

2.6

4,050

875

9.0

1.9

North Carolina

85,703

15,700

17.1

2.9

North Dakota

8,150

1,322

22.9

3.2

Nebraska

14,717

2,545

14.4

2.3

New Hampshire

15,828

2,216

29.4

3.4

New Jersey

39,593

8,904

9.4

2.0

Montana

New Mexico

10,604

2,512

9.2

2.1

Nevada

11,745

3,496

8.1

2.3

New York

115,632

19,477

12.6

2.0

Ohio

102,791

17,452

19.2

3.0

24,955

5,295

12.0

2.4

Oregon Pennsylvania

29,028 137,995

5,673 24,594

15.6 25.5

2.8 3.8

Rhode Island

13,447

1,685

28.4

3.1

South Carolina

43,229

7,283

18.0

2.8

South Dakota

2,623

653

6.0

1.5

Oklahoma

Tennessee

19,410

5,686

6.3

1.8

Texas

55,811

22,220

3.7

1.4

Utah

21,799

4,554

11.1

2.2

Virginia

42,948

10,919

11.4

2.7

Vermont

7,186

909

28.6

3.1

62,954

10,586

18.3

2.8

Washington

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Matern Child Health J Table 2 continued

State

Weighted frequency of children who received a home visit

SD

% Very young children (0–3 yrs.) who received a home visit

SE

Wisconsin

32,541

5,967

12.6

2.2

West Virginia

10,628

1,991

13.5

2.3

6,918

931

23.5

2.8

Wyoming

Legend 3.7% - 10.7% 10.8% - 18% 18.1% - 30.6%

Fig. 1 Weighted percent of children age 0-3 years who received a home visit with states divided into 3 quantiles

needs. Similarly, children born preterm or with low birth weight had odds of receiving HV 1.56 and 1.70 greater, respectively, than children born full-term or with typical birth weight. Moreover, children born at very low birth weight (\1,500 g) have greater than 3 times odds of receiving HV than a child within the normal range of birth weight. Other statistically significant predictors of receiving HV included risk factors for negative childhood outcomes. Specifically, a child with two or more ACEs has approximately 2 times greater odds of receiving HV than a child with no ACE. Children in families with household incomes less than 100 % FPL were more likely to receive HV than children living in households with higher annual income levels. Despite the association of poverty status with receiving HV services, children who live in more affluent states (\25 % of households below FPL) are more likely to receive HV services than their counterparts living in states

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with higher poverty rates. Children without health insurance were 25 % less likely to receive HV than children with public health insurance, and families with four or more children were 41 % less likely to receive HV than families with one child. A child whose mother was 20 years old or younger at the time of the child’s birth was 83 % more likely to receive HV than a child whose mother was older (Table 4).

Discussion Home visiting is a common approach for delivering services intended to assist with parenting, provide medical and developmental information, and identify families in need of additional services. Many studies have examined the effectiveness of individual programs, but no studies have taken a comprehensive look at the landscape of services

Matern Child Health J Table 3 Weighted characteristics of NSCH sample for children 0-3 years and children receiving a home visit Missing (n)

Sample % (n = 19,401)

Column % for HV (n = 2,995)

Row %

51.3

53.0

14.1

48.7

47.0

13.2

0

27.1

25.8

13.0

1 2

24.9 22.0

23.5 24.2

12.9 15.0

25.9

26.5

14.0

White, non-Hispanic

50.0

48.3

13.3

Black, non-Hispanic

12.1

16.2

18.5

Multi-racial/other, non-Hispanic

11.5

11.5

13.8

Hispanic

26.4

24.0

12.5

1 (only child)

31.1

32.7

14.3

2

37.5

36.6

13.3

3

21.4

21.7

13.8

9.9

9.1

12.5

Mother

71.2

76.3

14.6

Father Other

23.5 5.3

16.5 7.3

9.5 18.8

8.4

14.7

23.5

91.6

85.3

12.6

p value

Enabling and predisposing characteristics Child gender

13

Male Female Child age (years)

0

3 Child race

Total children in the household

Mother age at birth of study child (years)

0.011

0

0.782

\0.001

82

\0.001

852

B20 [20 Family structure

0.446

434

4 or more Respondent relationship

0.372

\0.001

243

Two parent-biological or adopted

77.1

68.5

12.1

1.9

3.0

21.7

17.5

24.1

18.9

3.5

4.4

17.1

\100 % FPL

27.1

37.9

19.1

100–199 % FPL

21.7

23.0

14.4

200–399 % FPL

25.7

17.2

9.1

400 % or more FPL

25.5

21.8

11.7

80.7 19.3

81.5 18.5

13.7 13.0

Public insurance

44.1

58.1

18.1

Private insurance

51.7

38.9

10.3

4.2

3.1

10.1

Low (\25 %)

44.5

51.4

15.8

High (C25 %)

55.5

48.6

12.0

Two parent-step family Single mother-no father present Other family type Household income

Primary language in household

21

English Language other than English Insurance type

0.671

\0.001

276

Uninsured State poverty rate

\0.001

0

\0.001

0

Need factors Gestation

61

\0.001

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Matern Child Health J Table 3 continued Missing (n)

Sample % (n = 19,401)

Column % for HV (n = 2,995)

Row %

Premature

12.2

21.8

24.4

Not premature

87.8

78.2

12.2

90.5

81.4

12.4

8.1

14.1

23.9

1.4

4.5

43.1

None

66.0

54.4

11.2

1 ACE

24.4

28.1

15.7

9.6

17.5

25.1

Child birthweight Low birthweight Very low birthweight Adverse childhood experiences

\0.001

229

2? ACEs Child Special Healthcare Needs

\0.001

362

Normal weight

\0.001

0

CSHCN Non-CSHCN

p value

9.0

16.1

24.4

91.0

83.9

12.6

NSCH National Survey of Child Health, FPL federal poverty level, ACE adverse childhood experience, CSHCN child with special health care needs

across the US. This study sought to fill this gap by providing accurate estimates of the number of US children receiving HV services, the topics most frequently discussed during HV visits, and the profile of children who are most likely to receive HV services. Estimating the HV Service Population This large, nationally representative survey yielded an estimated HV service population of more than 2.1 million young children (B3 years). Although previous estimates showed the HV service population doubled in the years between 1993 (200,000 children) and 2005 (i.e., 400,000 children) [9], clearly our estimate of 2.1 million children is much larger than what would be expected based on the trend in prior estimates. One explanation might be that given the wording of the NSCH question, the variety of services that respondents considered as HV services in this context was larger than the focus of prior estimates specifically on evidence-based early childhood HV models. Or, is it possible that the number of children receiving HV has increased fivefold during the past decade? Parents in this sample might not have received early-childhood HV services per se, but might have received home-based delivery of another program (e.g., home visit related to child care or Part C early intervention) or a single visit from a public health nurse. Should these experiences be included with HV models that rely on an ongoing supportive relationship with visits over multiple years? Definitions of HV models are imprecise, such as Johnson’s (2009) [18] definition that, rather than setting clear criteria, defines a HV program by asking three questions:

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(1) Does the program design assume home visits as the primary method for delivering the intervention? (2) Are a majority of services delivered/a majority of clients served through home visits? (3) Are staff trained to deliver services through home visits? The imprecise nature of this and other definitions of HV increases the possibility that the survey data we reported did not yield a valid estimate but have overestimated the population size. However, this is possibility is counterbalanced by the evidence of substantial growth in the number of HV programs and size of population served in recent years, due, at least in part, to MIECHV funding. The 2005 estimates were based on enrollment numbers from seven national programs [9], whereas there are currently 40 program models that have been reviewed by DHHS [3]. As the number of models expands with increased funding and policy support for HV, it is reasonable to assume that each model will serve an increasing number of families. Moreover, the two findings related to topics discussed in home visits—Table 1 list of most frequently discussed topics, of which 25 % of respondents reported discussing all seven topics—increases our confidence that these parents were reporting on program models similar to those on which prior estimates were based and focused on early-childhood evidence-based HV. Predictors of HV What remains unclear is whether the current level of HV service use is appropriate given the population needs. This study found children who face elevated risk for negative outcomes were more likely to have received HV. Poverty is a commonly cited risk for a host of negative outcomes in

Matern Child Health J Table 4 Results of multivariate GLMM predicting home visiting service use OR

Table 4 continued OR

95 % CI

1.00 1.56

1.13, 2.14

95 % CI Need factors

Enabling and predisposing characteristics

Gestation

Child gender Male

1.00

Female

0.87

Not premature Born premature 0.69, 1.09

Child age (years) 0

1.00

1 2

0.94 0.98

0.75, 1.19 0.73, 1.31

3

0.98

0.75, 1.26

Child race White, non-Hispanic

1.00

Child birthweight Normal weight

1.00

Low birthweight

1.70

1.16, 2.48

Very low birthweight

3.75

2.03, 6.93

Adverse childhood experiences None

1.00

1 ACE

1.29

0.97, 1.73

2? ACEs

2.06

1.35, 3.15

Black, non-Hispanic

1.25

0.90, 1.73

Multi-racial/Other, non-Hispanic

0.89

0.53, 1.49

Non-CSHCN

1.00

Hispanic

1.06

0.76, 1.48

CSHCN

1.93

Total children in the household 1 (only child)

1.00

2

0.91

Child Special Healthcare Needs 1.51, 2.47

0.75, 1.10

GLMM generalized linear mixed model, OR odds ratio, CI confidence interval, FPL federal poverty level, ACE adverse childhood experience, CSHCN child with special healthcare needs Estimates in bold are significant at p \ .05

3

0.91

0.70, 1.19

4 or more

0.63

0.48, 0.82

Respondent relationship Mother

1.00

Father Other

0.71 1.17

0.59, 0.85 0.70, 1.98

B20

1.83

1.45, 2.32

[20

1.00

Mother’s age at birth of study child (years)

Family structure Two parent-biological or adopted

1.00

Two parent-step family

1.28

Single mother-no father present

0.86

0.65, 1.15

Other family type

0.27

0.10, 0.74

0.50, 3.30

Household income \100 % FPL

1.00

100–199 % FPL

0.78

0.63, 0.97

200–399 % FPL

0.54

0.41, 0.71

400 % or more FPL

0.82

0.60, 1.13

1.00 1.16

0.68, 1.98

Primary language in household English Language other than English Insurance type Public insurance

1.00

Private insurance

0.77

0.61, 0.96

Uninsured

0.67

0.43, 1.05

Low (\25 %)

1.65

1.14, 2.39

High (C25 %)

1.00

State poverty rate

virtually every domain of child development [19, 20]. HV services appear to be addressing this risk in that, as compared with children from high SES households, children in low SES households (i.e., incomes below the FPL) were more likely to receive HV services. Similarly, a substantial body of research has shown that adults who experienced ACEs were more likely than their counterparts without ACE histories to experience numerous negative health, health behaviors, and psychosocial outcomes [21, 22]. This study found that children with two or more ACEs were significantly more likely to receive HV services. In addition, we found several factors predicted a child was more likely than others to have received HV services, including children at risk for poor birth outcomes (i.e., premature, low/very low birth weight), and children with complex, long-term special health care needs that elevate risk for negative outcomes such as subnormal growth; neurodevelopmental problems; and mild problems with cognition, attention, and neuromotor function [23]. These findings suggest that HV services are being targeted to children perceived as being at increased risk for negative childhood outcomes. HV is an avenue to help lowincome families identify children’s challenges and access intervention services as early as possible. Moreover, HV services can assist parents in working toward ameliorating their economic hardships. Equally important, given the link between ACEs and negative life outcomes, HV provides a

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critical opportunity to intervene early and interrupt the chain from ACEs to poor health outcomes. Finally, home visitors are able to provide support, assistance, and advice to families that are experiencing high levels of stress related to caring for a young child with complicated health needs. Families living in the more affluent states were more likely to report receiving HV services than families in states with fewer public resources. States with lower poverty rates likely have more resources available to invest in at-risk families. This state-level disparity is partially explained by environmental factors such as the ways in which states fund their HV programs and their broader maternal and child health system. A 2009 survey of 40 states indicated that HV programs are primarily funded with federal dollars, but the funding also includes a combination of state (including required match funds), local, and philanthropic sources. States and communities that subsidize HV beyond the limits of federal funding likely have programs with a broader reach, which might account for the increased access to HV in states with lower poverty rates. In addition, considerable variation exists in the way that HV services are coordinated and delivered across states. More affluent states are more likely to have both high-quality early-childhood systems of care and high-quality prenatal services, which would increase the reach and use of HV. Equally important, knowing which children are less likely to receive HV is critical to ensuring child well-being. Because the US does not have a universal HV program, there is a persistent perception that HV services are intended only for certain families. This perception has influenced the traditional allocation of HV services and is evidenced in the substantially lower rates of HV received by fathers, older mothers, and families with multiple children. However, some HV use might be under-reported because certain survey respondents (e.g., fathers) were not aware that HV services were received for the subject child. In addition, due to the lack of funding to support universal models, at least, in part, most HV programs have imposed strict eligibility criteria to limit access and control costs. Selective programs use a targeted prevention approach to focus services based on prior evidence and theory [24]. For example, the Nurse-Family Partnership program serves only low-income first-time mothers. Various HV program models have been designed to serve many different types of families. The question is whether these programs are available in the continuum of services available in a given community. Although it is reasonable to target scarce resources to at-risk families, continuing to follow traditional patterns of allocations can overlook groups with the potentially highest risk of poor outcomes, such as low-income mothers with multiple children [25]. Children with private insurance plans are also less likely to receive HV as compared with children with public

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insurance such as Medicaid. This finding holds true even when controlling for factors such as income level and infant need. This finding suggests demand for HV services might increase among middle- and higher-income families if private insurance plans covered HV and if providers referred families to HV. The perception of HV services as a public service intended for low-income, high-risk families creates stigma and impedes the broader use of this preventive health service. Widespread dissemination of HV will require policies that promote a prevention model of universal access to HV services with targeted recruitment of and/or more intensive services for higher-risk families. Study Limitations Several limitations must be considered when interpreting the results of this study. The NSCH asked whether a parent had received any kind of home visit from a number of different types of professionals. Given this broad language, we were unable to differentiate between types of HV programs, and therefore, consider them as a group. Differentiating between HV programs would be useful in understanding how programs can be better targeted to meet the needs of specific subgroups and thereby improve program efficacy. For example, as compared with families with a single child, families with multiple children were less likely to receive HV; this service use pattern could likely be explained by identifying the type of programs available and their eligibility criteria. That is, some programs accept only first-time mothers (e.g., Nurse–Family Partnership), whereas other programs (e.g., Parents as Teachers) serve mothers and fathers with multiple children. However, because we examined all early-childhood HV as a group, we were unable to assume eligibility criteria were the factors driving the association between family size and HV receipt. In addition, based on our concerns regarding our ability to determine comparable figures across states, we did not include the amount of funding provided by each state for HV services. Additional research is needed to compile accurate estimates of service population changes across HV models over time as well as policy changes that might affect differences in penetration rates across a state. Future survey research could assess the number of visits received for each program model by name, or ask the respondent to provide the program name. The strength of the estimate provided by this study is that it reflects a comprehensive definition of HV. Another important limitation to the current study stems from the cross-sectional nature of the NSCH data, which allowed us to provide only point-in-time estimates of the characteristics associated with receiving HV services. To avoid reverse causation, we examined those characteristics that would likely precede involvement in HV services; that

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is, we excluded certain variables (e.g., child behavioral problems) that might influence the propensity to receive HV services as well as might be influenced by the receipt of HV services. Despite these efforts, some variables typically viewed as static (e.g., individual poverty status) could be affected by HV services such as those focused on reducing economic hardship and infant health outcomes for prenatal HV. Therefore, although we have interpreted the study results as increasing the likelihood of receiving HV services, the possibility remains that some of the factors investigated might be driven by virtue of receiving HV services.

Conclusion The current study has several important findings. First, HV services appear to be reaching families with elevated risk of poor outcomes. To the extent that policymakers aim to fund programs targeted at children with higher levels of need, the current system reflects that goal. The state-level finding that children in states with higher levels of poverty (and fewer public resources) are less likely to receive a HV suggests policy makers should consider allocating additional resources to these states to provide HV services to children in greatest need. Finally, although HV services are targeted to children with high levels of need, only an estimated 19 % of low-income children (about 1 in 5) received HV. Further, 24 % of preterm infants and 43 % of infants born with very low birth weight received HV. Although not every child in a low-income family or those with infant risk factors might need HV, the percentage of children who could benefit from preventative HV services is likely far greater than the percentage receiving HV services. Therefore, increasing the level of HV services available in the continuum of services available in the community will likely benefit child health and well-being.

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