prenatal home visit

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What happens during prenatal visits?

What happens during prenatal visits varies depending on how far along you are in your pregnancy.

Schedule your first prenatal visit as soon as you think you are pregnant, even if you have confirmed your pregnancy with a home pregnancy test. Early and regular prenatal visits help your health care provider monitor your health and the growth of the fetus.

The First Visit

Your first prenatal visit will probably be scheduled sometime after your eighth week of pregnancy. Most health care providers won't schedule a visit any earlier unless you have a medical condition, have had problems with a pregnancy in the past, or have symptoms such as spotting or bleeding, stomach pain, or severe nausea and vomiting. 1

You've probably heard pregnancy discussed in terms of months and trimesters (units of about 3 months). Your health care provider and health information might use weeks instead. Here's a chart that can help you understand pregnancy stages in terms of trimesters, months, and weeks.

Because your first visit will be one of your longest, allow plenty of time.

During the visit, you can expect your health care provider to do the following: 1

• Answer your questions. This is a great time to ask questions and share any concerns you may have. Keep a running list for your visit.
• Check your urine sample for infection and to confirm your pregnancy.
• Check your blood pressure, weight, and height.
• Calculate your due date based on your last menstrual cycle and ultrasound exam.
• Ask about your health, including previous conditions, surgeries, or pregnancies.
• Ask about your family health and genetic history.
• Ask about your lifestyle, including whether you smoke, drink, or take drugs, and whether you exercise regularly.
• Ask about your stress level.
• Perform prenatal blood tests to do the following:
• Determine your blood type and Rh (Rhesus) factor. Rh factor refers to a protein found on red blood cells. If the mother is Rh negative (lacks the protein) and the father is Rh positive (has the protein), the pregnancy requires a special level of care. 2
• Do a blood count (e.g., hemoglobin, hematocrit).
• Test for hepatitis B, HIV, rubella, and syphilis.
• Do a complete physical exam, including a pelvic exam, and cultures for gonorrhea and chlamydia.
• Do a Pap test or test for human papillomavirus (HPV) or both to screen for cervical cancer and infection with HPV, which can increase risk for cervical cancer. The timing of these tests depends on the schedule recommended by your health care provider.
• Do an ultrasound test, depending on the week of pregnancy.
• Offer genetic testing: screening for Down syndrome and other chromosomal problems, cystic fibrosis, other specialized testing depending on history.

Prenatal Visit Schedule

If your pregnancy is healthy, your health care provider will set up a regular schedule for visits that will probably look about like this: 1

Later Prenatal Visits

As your pregnancy progresses, your prenatal visits will vary greatly. During most visits, you can expect your health care provider to do the following:

• Check your blood pressure.
• Measure your weight gain.
• Measure your abdomen to check your developing infant's growth—"fundal height" (once you begin to "show").
• Check the fetal heart rate.
• Check your hands and feet for swelling.
• Feel your abdomen to find the fetus's position (later in pregnancy).
• Do tests, such as blood tests or an ultrasound exam.

Talk to you about your questions or concerns. It's a good idea to write down your questions and bring them with you.

Several of these visits will include special tests to check for gestational diabetes (usually between 24 and 28 weeks) 3 and other conditions, depending on your age and family history.

In addition, the Centers for Disease Control and Prevention and the American Academy of Pediatrics released new vaccine guidelines for 2013 , including a recommendation for pregnant women to receive a booster of whooping cough (pertussis) vaccine. The guidelines recommend the shot be given between 27 and 36 weeks of pregnancy. 4

• Centers for Disease Control and Prevention. (2013). Updated recommendations for use of tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis vaccine (TDAP) in pregnant women―Advisory Committee on Immunization Practices (ACIP), 2012. Retrieved September 20, 2013, from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6207a4.htm

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When does the First Prenantal Visit Happen, and what can You Expect?

You’ve been seeing a gynecologist for years, and at this point, you’ve got the routine of those wellness exams down pat.  But, this time is different.  This time, you aren’t going for a checkup.  Instead, you’ll be headed in for your very first obstetrics appointment as an expectant mom.  Congratulations!

Seeing a home pregnancy test turn positive for the first time can be a thrilling moment, but it also marks the beginning of a completely new and unfamiliar journey.  You’ll have lots of questions along the way, and one of the first will be when to actually see your OB.  While, you may be expecting to get in right away, it could be a few weeks before that first prenatal visit occurs.  Here are the details of when you will most likely have your initial office visit and what to expect:

When will You have Your First Prenatal Appointment?

As soon as you learn of your pregnancy, you should   schedule your first prenatal visit .  If you are considered healthy and low-risk, you can expect to be scheduled around 8 weeks.  However, there are cases when you may see your doctor sooner. For instance, if you want to confirm your pregnancy, you can be seen for a pre-OB appointment in which more sensitive blood tests can back up the results of your at-home test.  In other scenarios, problematic symptoms such as bleeding or abdominal pain or a medical history that places you in a high-risk category may result in earlier prenatal visits. 

What can You Expect at Your First Prenatal Appointment?

The first prenatal visit is the most comprehensive and often takes the longest.  Much information regarding you, your baby, and the road ahead will be covered.  It’s a good idea to come into this appointment prepared with your own list of questions or concerns, and you may wish for your partner to attend as well.  In general, here is what you can expect:

Health History –   Your OB will need to know any details surrounding your health that could potentially impact you or your unborn baby.  Expect to be asked about chronic health conditions, gynecological health history, family medical history,   medications , and habits such as   smoking   and alcohol use.

Checkup and Tests –   During the initial visit, you will be given urine and blood tests to confirm your pregnancy, screen for conditions such as anemia, and determine blood type and   Rh status . You will also receive a general health exam that includes obtaining your baseline blood pressure and weight.  Unless you’ve had one completed recently, you may receive a pap smear as well.

Due Date –   While you may already be well aware of this date through online research, your OB will confirm your expected due date.

General Pregnancy Information and Q&A –   Your provider will offer you guidance regarding healthy habits during pregnancy.  These can include healthy diet, what to avoid, and an overview and possible prescription for prenatal vitamins.  He or she will also review common symptoms or discomforts that can occur in early pregnancy, as well as which symptoms may be cause for concern.  This time will also be your opportunity to ask any questions or raise any concerns which have not already been addressed regarding your   prenatal care and what to expect throughout your pregnancy .

Discovering you are pregnant can be both exciting and overwhelming.  However, a great OB will not only be able to provide excellent care for you and your baby, they will also be able to address all your concerns and put your mind at ease.  While you may be anxious beforehand, you should leave your initial prenatal appointment feeling more confident and comfortable. 

If you are in the Zachary area, contact   Bayou Regional Women’s Clinic   and request your first appointment with one of our three,   highly qualified OB/GYN physicians .

Bayou Regional Women’s Clinic

6550 Main Street, Suite 2000

Zachary, LA  70791

(225) 658-1303

Topics: GYN , Labor , BabyLane

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Remote and in-home prenatal care: Safe, inclusive, and here to stay

Dr. Denny is Clinical Assistant Professor, Department of Obstetrics and Gynecology, New York University Grossman School of Medicine, New York, New York, and Medical Director of Ambulatory Women’s Health Services, Bellevue Hospital Center, New York, New York.

Dr. Goldstein is Professor of Obstetrics and Gynecology, Department of Obstetrics and Gynecology, New York University Grossman School of Medicine.

The authors report no financial relationships relevant to this article.

A look at how the COVID-19 pandemic has reshaped the prenatal treatment model for the benefit of patients and clinicians

For much of the general public, in-home care from a physician is akin to the rotary telephone: a feature of a bygone age, long since replaced by vastly different systems. While approximately 40% of physician-patient interactions in 1930 were house calls, by the early 1980s this had dwindled to less than 1%, 1 with almost all physician-patient encounters taking place in a clinical setting, whether in a hospital or in a free-standing clinic. In the last 2 decades, a smattering of primary care and medical subspecialty clinicians started to incorporate some in-home care into their practices in the form of telemedicine, using video and telephone technology to facilitate care outside of the clinical setting, and by 2016, approximately 15% of physicians reported using some form of telemedicine in their interactions with patients. 2

Despite these advances, prior to the COVID-19 pandemic, obstetricians lagged significantly behind in their use of at-home or remote care. Although there were some efforts to promote a hybrid care model that incorporated prenatal telemedicine, 3 pre-pandemic ObGyn was one of the least likely fields to offer telemedicine to their patients, with only 9% of practices offering such services. 2 In this article, we discuss how the COVID-19 pandemic resulted in a shift from traditional, in-person care to a hybrid remote model and how this may benefit obstetrics patients as well as clinicians.

Pre-pandemic patient management

The traditional model of prenatal care presents a particularly intense time period for patients in terms of its demands. Women who are pregnant and start care in their first trimester typically have 12 to 14 visits during the subsequent 6 to 7 months, with additional visits for those with high-risk pregnancies. Although some of these visits coincide with the need for in-person laboratory work or imaging, many are chiefly oriented around assessment of vital signs or counseling. These frequent prenatal visits represent a significant commitment from patients in terms of transportation, time off work, and childcare resources—all of which may be exacerbated for patients who need to receive their care from overbooked, high-risk specialists.

After delivery, attending an in-person postpartum visit with a newborn can be even more daunting. Despite the increased recognition from professional groups of the importance of postpartum care to support breastfeeding, physical recovery, and mental health, as many as 40% of recently delivered patients do not attend their scheduled postpartum visit(s). 4 Still, before 2020, few obstetricians had revised their workflows to “meet patients where they are,” with many continuing to only offer in-person care and assessments.

COVID-19: An impetus for change

As with so many things, the COVID-19 pandemic has challenged our ideas of what is normal. In a sense, the pandemic has catalyzed a revolution in the prenatal care model. The very real risks of exposure and contagion during the pandemic—for clinicians and patients alike—has forced ObGyns to reexamine the actual risks and benefits of in-person and in-clinic prenatal care. As a result, many ObGyns have rapidly adopted telemedicine into practices that were strictly in-person. For example, a national survey of 172 clinicians who offered contraception counseling during the pandemic found that 91% of them were now offering telemedicine services, with 78% of those clinicians new to telemedicine. 5 Similarly, although a minority of surveyed obstetricians in New York City reported using telemedicine pre-pandemic, 89% planned to continue using such technology in the future. 6

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Treatment Conditions

Main outcomes, statistical models and methods of analysis, cognitive, language, achievement, and executive functioning outcomes, behavioral health, conclusions, acknowledgments, prenatal and infancy nurse home visiting and 18-year outcomes of a randomized trial.

POTENTIAL CONFLICT OF INTEREST: The Prevention Research Center for Family and Child Health, directed by Dr Olds at the University of Colorado School of Medicine, has a contract with the Nurse-Family Partnership (NFP) to conduct research to improve the NFP program and its implementation; this contract covers part of Dr Olds’s salary and part of Mr Knudtson’s salary. Dr Olds and Mr Knudtson were employed by this center at the time the study was conducted. Dr Olds is the founder of NFP and, with the University of Colorado, owns the NFP intellectual property. The University of Colorado receives royalties from governments and organizations outside of the United States that implement NFP and has contracts with those entities to guide the implementation of NFP with quality, but none of the royalties or fees go to Dr Olds personally; they are used to support the Prevention Research Center for Family and Child Health research and implementation guidance. Dr Miller performs economic analyses under contract for the nonprofit Nurse-Family Partnership National Service Office; the other authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: Dr Olds receives personal honoraria and travel expenses from philanthropies and organizations for speaking about the Nurse-Family Partnership and early intervention; the other authors have indicated they have no financial relationships relevant to this article to disclose, beyond those already listed in the Potential Conflict of Interest.

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Harriet Kitzman , David L. Olds , Michael D. Knudtson , Robert Cole , Elizabeth Anson , Joyce A. Smith , Diana Fishbein , Ralph DiClemente , Gina Wingood , Angela M. Caliendo , Christian Hopfer , Ted Miller , Gabriella Conti; Prenatal and Infancy Nurse Home Visiting and 18-Year Outcomes of a Randomized Trial. Pediatrics December 2019; 144 (6): e20183876. 10.1542/peds.2018-3876

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Video Abstract

Given earlier effects found in randomized clinical trials of the Nurse-Family Partnership, we examined whether this program would improve 18-year-old first-born youths' cognition, academic achievement, and behavior and whether effects on cognitive-related outcomes would be greater for youth born to mothers with limited psychological resources (LPR) and on arrests and convictions among females.

We enrolled 742 pregnant, low-income women with no previous live births and randomly assigned them to receive either free transportation for prenatal care plus child development screening and referral (control; n = 514) or prenatal and infant home nurse visit (NV) plus transportation and screening ( n = 228). Assessments were completed on 629 18-year-old first-born offspring to evaluate these primary outcomes: (1) cognitive-related abilities (nonverbal intelligence, receptive language, and math achievement) and (2) behavioral health (internalizing behavioral problems, substance use and abuse, sexually transmitted infections, HIV risk, arrests, convictions, and gang membership).

Compared with control-group counterparts, NV youth born to mothers with LPR had better receptive language (effect size = 0.24; 95% confidence interval [CI]: 0.00 to 0.47; P = .05), math achievement (effect size = 0.38; 95% CI: 0.14 to 0.61; P = .002), and a number of secondary cognitive-related outcomes. NV females, as a trend, had fewer convictions (incidence ratio = 0.47; 95% CI: 0.20 to 1.11; P = .08). There were no intervention effects on other behaviors.

The program improved the cognitive-related skills of 18-year-olds born to mothers with LPR and, as a trend, reduced female convictions but produced no other effects on youth behavioral health.

Two randomized trials of prenatal and infant home visit by nurses found effects on children’s behavioral problems, early adolescent substance use, and among children born to mothers with limited psychological resources, cognitive outcomes. One trial found fewer convictions among females.

This trial extends earlier estimates of intervention impact: compared with control-group counterparts, nurse-visited 18-year-olds born to mothers with limited psychological resources exhibited better cognitive functioning; females, as a trend, had fewer convictions. There were no significant effects on behavioral health.

The potential of early intervention to improve the lives of children born into disadvantaged families has gained considerable attention. 1 , 2   Pregnancy and the early years of life are opportune times to intervene because of significant neuroendocrine changes in mothers, developing fetuses, and young children. 3 , 4   Prenatal and early childhood exposures to toxicants, maltreatment, and stress are thought to amplify one another over time, contributing to compromised life-course development and making this an opportune time to intervene to improve vulnerable children’s development. 5   Our team has been conducting a series of pragmatic randomized clinical trials (RCTs) of a program of prenatal, infant, and toddler home visiting by nurses for low-income mothers and their children known as the Nurse-Family Partnership (NFP). 6 – 20   The current study assesses 629 primarily African American first-born 18-year-old youth whose mothers participated in the second RCT of NFP in Memphis, Tennessee. 11 – 17  

Findings from the current trial 11 – 16   and NFP trials conducted earlier in Elmira, New York, 6 – 10   and later in Denver, Colorado, 18 – 20   led us to hypothesize that NFP would improve 18-year-old youth language, cognition, and math achievement and that effects in this broad domain would be most pronounced for youth born to mothers with limited psychological resources (LPR) to cope with adversity: in the lower half of the distribution on an index composed of maternal intellectual functioning, 21   mental health, 22   and sense of mastery 23   plus self-efficacy (mothers’ beliefs about the importance of and her confidence in accomplishing key NFP behavioral objectives) 24   measured at baseline. 11  

Given NFP effects on substance use and antisocial behavior through age 15 among youth born to disadvantaged mothers in the Elmira trial 9   and substance-use and internalizing disorders in the current trial at age 12, 15   we hypothesized that the program would reduce 18-year-olds’ internalizing disorders, substance use, and abuse disorders. In light of intervention effects on the number of lifetime sex partners through age 15 among youth born to disadvantaged mothers in Elmira 9   and anticipated effects on substance abuse disorders in the current trial, we hypothesized that nurse visited (NV) 18-year-olds would have fewer pregnancies and births and a lower risk for HIV. Given NFP effects on arrests, convictions, and violations of probation among 15-year-olds born to disadvantaged mothers in the Elmira trial 9   and substance use at age 12 in the current trial, 15   we hypothesized that the program would reduce rates of gang membership, arrests, and convictions among 18-year-olds.

Before analysis of intervention-control differences, we found that program effects on arrests and convictions in Elmira were limited to females through age 19, 10   leading us to hypothesize the presence of corresponding female effects in Memphis. Also, given the presence of little meaningful variation in neighborhood disadvantage (2.4 SDs above the national mean 25   ), we eliminated, before analysis of intervention effects, a hypothesis that program effects would be greater among youth whose mothers lived in the most distressed neighborhoods at registration.

The basic features of this study have been reported earlier. 11   We conducted this RCT in a public system of obstetric and pediatric care in Memphis, Tennessee, with registration of the original sample completed between June 1990 and August 1991. Given that program effects were more pronounced for mothers and children from more disadvantaged families in the preceding trial, 6 – 10   we focused sampling in Memphis on those with overlapping sociodemographic risks. We enrolled women <29 weeks’ gestation with no previous live births and at least 2 sociodemographic risks (unmarried, <12 years of education, or unemployed). Ninety-two percent of the women were African American, and at enrollment, 98% were unmarried, 64% were <18 years of age, and 85% were from households with incomes below the federal poverty guidelines. For the current follow-up, participating mothers, other caregivers, and youth completed informed consent procedures approved by the University of Rochester Institutional Review Board.

Table 1 summarizes the Consolidated Standards of Reporting Trials information. Eighty-eight percent ( n = 1138) of 1289 eligible pregnant women who were offered participation completed informed consent and were randomly assigned to 1 of 4 treatment conditions following a procedure that concealed assignment from individuals involved in gathering participant data. 11   We assigned 742 participants to 2 treatment conditions created to estimate program effects on postnatal outcomes: 514 to Treatment 2 (control) and 228 to Treatment 4 (NV), both described below. Sample size and assignment ratios were derived from statistical power calculations in the original phase of the trial. 11     Table 1 shows those lost to follow-up because of miscarriage or child death, maternal or child refusal to participate at earlier phases, and the number evaluated with youth assessments and maternal and/or other-custodian interviews at youth age 18.

Consolidated Standards of Reporting Trials Information for Youth Enrolled in the Trial Through 18-Year Follow-up

There were 1290 subjects eligible to participate; 151 declined participation, and 1138 were randomly assigned. CBCL, Child Behavior Checklist; —, not applicable.

Treatment 1: prenatal transportation.

Treatment 2: prenatal transportation plus developmental screening and referral.

Treatment 3: prenatal transportation plus developmental screening and referral and prenatal nurse home visits.

Treatment 4: prenatal transportation plus developmental screening and referral and prenatal and infant and/or toddler nurse home visits.

Note that 1 mother was enrolled and randomly assigned twice by mistake after a miscarriage. We included her only once, with her original assignment, in the control group.

Some outcomes reported in Tables 4 and 5 show higher numbers than indicated in this table because they include repeated estimates of outcomes from earlier phases of follow-up, as indicated by “all” under “age at assessment” in those tables.

Some youth were unable or unwilling to complete the standardized psychological or achievement assessments or to provide urine to screen for STIs or substances, reducing the numbers shown in Tables 4 and 5 .

Youth arrests were derived from a detailed examination of self-report, maternal, and other-caregiver report, synthesizing data from all 3 data sources (when available); this shows the number of cases for which data were gathered from maternal and/or other-caregiver report. Estimates of arrest-related outcomes were made even if maternal and other-caregiver reports were unavailable.

Youth CBCL externalizing and total behavioral problems (borderline or clinical) were derived from a combination of youth self-report and either maternal or other-caregiver report. Maternal report was given priority over other-caregiver report.

Some youth were unable or refused to provide urine for substance-use or STI screening. One sample was unusable for STI screening.

Interviews for this follow-up were conducted between October 2008 and September 2014, and reviews of school records were conducted by December 2015 by staff masked to treatment assignment. Most assessments were conducted after youth 18th birthdays (mean age 18.67 years; SD = 0.95; range: 17.5–23.9). Repeated measures of some outcomes over time increased the numbers and are noted by “all” under the column "Child Age ge at Assessment” in Tables 3 and 4 .

Interviews were completed with 629 of the 669 available youth. Interviews were conducted with mothers and other caregivers to augment youth report of arrest outcomes ( n = 621) and externalizing and total behavior problems ( n = 615) and to determine Supplemental Social Security Income (SSI) for disability ( n = 619). High school graduation records were collected for 619 youth, and urine was collected for 606 youth.

Women in the control group ( n = 514) were provided with free transportation for scheduled prenatal care plus developmental screening and referral for children at 6, 12, and 24 months of age. Women in the NV condition ( n = 228) were provided with the same services as the control group plus prenatal and infancy home visitation through age 2.

NFP was designed to (1) improve pregnancy outcomes by promoting women’s prenatal health behaviors, (2) improve children’s health and development by promoting parents’ care of their children, and (3) enhance parents’ health and life course by encouraging women to plan the timing of subsequent pregnancies, complete their educations, and find work. Nurses linked families with needed services and, when possible, involved other family members (especially children’s fathers and grandmothers). 5   Program protocols were grounded in developmental epidemiology and theories of human attachment, human ecology, and self-efficacy and adjusted to families’ individual needs. 5  

The program was implemented by the Memphis and Shelby County Health Department during a nursing shortage, leading to nurse turnover for 37% of the families. 11   Nurses carried a maximum caseload of 25 families each and relied on detailed visit-by-visit guidelines structured around 62 home visits. It is impossible for nurses to complete 62 visits for all families, and most families do not need this level of service. Nurses used their clinical judgment to adjust dosage and visit content, as well as telephone communications when in-person visits were not possible, to address individual needs revealed in the conduct of visits.

All families were scheduled to receive 4 weekly visits at the beginning of the program to facilitate nurses’ and mothers’ getting to know one another as early in the pregnancy as possible and to develop a trusting relationship founded on nurses’ understanding of mothers’ aspirations and concerns about their prenatal health, the developing fetus, birth, and the challenges of caring for a newborn. Newborn health and mother’s adjustment to caregiving were critical factors in nurses’ decisions about visit dose and content. Nurses recorded features of program implementation on every attempted and completed visit. 5 , 26 , 27  

Overall, nurses completed a mean of 7 home visits during pregnancy and 26 visits during the first 2 years postpartum. 11 , 27   Mothers in the lowest quartile of psychological resources (PR) at baseline received the highest number of home visits (mean = 37.67; SE = 2.38), those in the middle 2 quartiles had the fewest (mean = 32.02; SE = 1.68), and those in the highest quartile of PR received an intermediate number of visits (mean = 34.26; SE = 2.38).

Table 2 shows that outcomes were divided into 2 broad categories: (1) cognitive-related outcomes (nonverbal intelligence, language, math achievement, sustained attention, working memory, emotion recognition, risky decision-making, SSI for disability, high school graduation, and graduation with honors) and (2) behavioral health (mental health [internalizing, externalizing, and total behavioral problems], substance use and abuse, sexually transmitted infections [STIs], HIV risk, arrests and convictions, and gang membership). Within these broad categories, we separated primary from secondary outcomes. Table 2 shows the specific measures used and bases for hypotheses. Primary outcomes were predicted from previous intervention effects on the same measures or constructs in earlier phases of the current trial or other NFP trials and, for some outcomes, from effects found at earlier phases. Secondary outcomes were selected on the basis of their epidemiological and theoretical associations with earlier effects in the Elmira, Memphis, or Denver trials. 5 – 9 , 11 – 16 , 18 – 20   We included 2 exploratory outcomes: high school graduation with honors and mother and/or caregiver report of youth receipt of SSI for disability.

Outcome Domains, Variables Measured, and Bases for Hypotheses

CIDI-SAM, Composite International Disease Interview–Substance Abuse Model; WAIS, Wechsler Adult Intelligence Scale; —, not applicable.

We show the bases for hypotheses in 3 categories: (1) an earlier effect on the same specific measure or construct in an earlier phase of the trial, (2) an effect on the same measure or construct in other trials, and (3) effects in earlier phases or trials that predict the current outcomes on theoretical or epidemiological grounds. When the prediction was made from the same measure, the basis for the hypothesis is shown on the same row; otherwise, it is shown on the construct row. Note that those outcomes hypothesized to be greater for particular subgroups are shown in the last column.

Outcome domain. Specific variables assessed are shown under each outcome domain. Outcomes were selected on the basis of their being affected in earlier phases of this trial or the preceding trial or on theoretical and epidemiological grounds, with attention paid to those aspects of functioning that are of clinical or public health importance and that could be assessed without overburdening respondents.

Subgroup defined by youths' mothers at registration 11   falling into the lower half of the distribution of an index composed of the average z scores of women’s intellectual functioning, 21   mental health, 22   and sense of mastery 23   plus self-efficacy (based on mothers’ confidence in their ability to accomplish key NFP behavioral objectives). 24  

The intervention effect on nonverbal intelligence at age 6 was a trend overall and for the LPR group, derived from the Kaufman Assessment Battery for Children Mental Processing Composite. 29   The analysis of the whole-scale Kaufman Assessment Battery for Children Mental Processing Composite was significantly different for the treatment-control contrast, both overall and for the LPR group at age 6. 13   The nonverbal subscale trend was not reported in the earlier publication. 13   The Kaufman Brief Intelligence Test 2 administered at age 18 is a shortened version of intellectual functioning based on the full-scale Kaufman Assessment Battery for Children.

Exploratory outcome. Graduating with honors was not part of the original measurement design per se. The original plan called for gathering school records of grade point averages and disciplinary records, which were incomplete, leading us to eliminate them as part of the final measurement design. We discovered, however, that states sent information on graduation with honors, which aligns with the original measurement design and thus is included in this report. Note that all states to where participants moved record graduating with honors, with the exception of Mississippi. Seventeen participants graduated from Mississippi schools (10 control; 7 NV); they are included in the analysis as not having graduated with honors.

Exploratory outcome. SSI (disability) was based on parent and/or caregiver report and not included in the original measurement design. The question is relevant to this report and thus is included here.

Data analyses are reported on all cases randomized insofar as outcome data were available. The analysis adhered to a statistical analysis plan established before examination of data from the intervention group.

The primary statistical model for cognitive-related outcomes consisted of a 2-level treatment factor (control versus NV) and a 2-level factor reflecting mothers’ PR (above versus below the sample median), focusing on treatment differences for the LPR group, in models that included 3 covariates (household poverty index, maternal attitudes predictive of child abuse [CAA], 43   and youth sex). The first 2 covariates, consistent predictors of a range of outcomes, adjusted for treatment nonequivalence at registration; the third was added because of its strong relationship with some outcomes. The household poverty index incorporates 3 variables: discretionary household income, housing density, and head-of-household employment. For emotion-recognition analyses, we added age-18 nonverbal intelligence to the model to reduce the likelihood that intervention effects would simply reflect differences in intelligence.

For arrest and conviction outcomes, we examined NV-control differences in a model that included sex as a classification factor, examining treatment differences separately for females and males, in models that included 3 covariates: maternal PR, household poverty, and CAA. 43  

Given that rates of pregnancies, births, and STI outcomes were operationalized differently for males and females, we examined NV-control differences separately for males and females and included 3 covariates: maternal PR, household poverty, and CAA. 43  

For analysis of HIV risk, we examined NV-control differences in a model that included covariates for maternal PR, youth sex, youth age at assessment, household poverty, and CAA. 43  

For all remaining behavioral health outcomes, we examined NV-control differences in models that included covariates for maternal PR, youth sex, household poverty, and CAA. 43  

For continuous and dichotomous outcomes on which we had repeated assessments for each child over time, we analyzed outcomes using generalized mixed models that included, in addition to variables from the primary model, children as levels of a random factor, a fixed repeated-measures classification factor for time of assessment, and all interactions of time with the other fixed classification factors.

Continuous dependent variables were analyzed in the general linear model, and dichotomous outcomes were analyzed in the logistic linear model. For low-frequency count outcomes, we analyzed data in generalized linear models with negative binomial error assumptions. We examined low-frequency outcomes with rerandomization tests to determine model fit 44   and truncated 1 outlier for the count-of-convictions outcome. Substance-use disorders and timing to first pregnancy and birth were analyzed over time by using Cox proportional-hazards models. We present survival rates at age 18 along with hazard ratios.

Supplemental Tables 6 and 7 show estimates of NV-control differences without covariate adjustments.

The NV and control groups were similar on background characteristics for participants for whom 18-year follow-up assessments were conducted ( Table 3 ), with these exceptions: at intake, NV women, compared with controls, lived in households with less discretionary income, higher person-per-room density, higher scores on a household poverty index, and higher scores on CAA. 43  

Background Characteristics at Randomization of Those Participants for Whom 18-Year Assessments Were Completed

T2, prenatal transportation plus developmental screening and referral.; T4, prenatal transportation plus developmental screening and referral and prenatal and infant and/or toddler nurse home visits.

Subgroup defined by youths' mothers falling into the lower half of the distribution for PR (LPR) described in the following footnote.

Average z scores of women’s intellectual functioning, 21   mental health, 22   and sense of mastery 23   plus self-efficacy 24   (mastery and self-efficacy measures were standardized and averaged; self-efficacy is based on participants’ beliefs about the importance of and confidence in accomplishing key NFP behavioral objectives).

Standardized to sample: mean = 100; SD = 10.

Annual household discretionary income is based on income subsistence standards for Medicaid eligibility, reported household income, and number of individuals in the household at registration.

Persons per room.

Locally developed scale that assesses degree to which the mother experiences conflict in her relationship with this person.

Adult-Adolescent Parenting Inventory. 43  

Average z scores of household discretionary income, housing density, and whether the head of household was employed.

Average of variables calculated in SD units from the national means of components that comprise the index of concentrated social disadvantage (percentage of block group residents: [1] below federal poverty level, [2] receiving public assistance, [3] unemployed, [4] headed by single women, [5] age <18, and [6] African American). 25  

Table 4 summarizes estimates of youth functioning in the cognitive, language, achievement, and executive functioning domains for the sample as a whole and for youth born to mothers with LPR. With the exception of NV youth having higher rates of graduation with honors (adjusted odds ratio [aOR] = 2.12; 95% confidence interval [CI]: 1.09 to 4.13; P = .028) and trends ( P < .10) of NV youth having higher math scores and better emotion recognition, there were no intervention-control differences for the sample as a whole. NV children born to mothers with LPR, on the other hand, had better receptive language (effect size [ES] = 0.24; 95% CI: 0.00 to 0.47; P = .048) and math achievement (ES = 0.38; 95% CI = 0.14 to 0.61; P = .002) at age 18. For both of these outcomes, there were longitudinal effects over time. There were no intervention-control differences in nonverbal intelligence.

Estimates of Cognitive, Language, Academic, and Executive Functioning Outcomes and SSI Benefit Received Among 18-Year-Old Youth in the Intervention and Control Conditions

The estimates of intervention-control differences averaged over all other fixed classification variables, including those within subjects, and the same treatment effect were restricted to the group defined by LPR. This table shows the least-squares means at 18 y and repeated measures over time (labeled “all” under “age at assessment”), which also are averaged over other fixed classification effects. For estimates of treatment effects based on repeated measures, we assumed an error structure with different variances at each time for a given child and a different covariance between pairs of times within each child. These were assumed to be the same for all children, and covariance between children was assumed to be negligible. Contrasts at specific, earlier time points are presented in earlier publications. 11 – 16   LS, least squares; T2, prenatal transportation plus developmental screening and referral.; T4, prenatal transportation plus developmental screening and referral and prenatal and infant and/or toddler nurse home visits.

Age 18 assessment denoted by 18 y; repeated-measures assessment denoted by “all.” The exact ages aggregated for repeated-measures estimates are given in footnotes.

Expressed in SD units.

Subgroup defined by youth mothers falling into the lower half of the distribution for PR (LPR).

Ages 6 and 18.

Ages 6, 12, and 18.

Ages 12 and 18.

Exploratory outcome.

P < .10; ** P < .05; *** P < .01.

NV children born to mothers with LPR also had better working memories (ES = 0.23; 95% CI = 0.01 to 0.46; P = .045) and emotion-recognition abilities (ES = 0.22; 95% CI: 0.01 to 0.44; P = .040), lower SSI for disability (aOR = 0.33; 95% CI: 0.13 to 0.84; P = .011), and higher rates of high school graduation with honors (aOR = 3.34; 95% CI: 1.19 to 9.34; P = .022) than their control-group counterparts.

There were no intervention-control differences in children’s sustained attention, risky decision-making, or high school graduation.

Table 5 shows that NV females, as a trend, had fewer criminal convictions than control females (incidence ratio [IR] = 0.47; 95% CI 0.20 to 1.11; P = .080). There were no intervention-control differences in internalizing, externalizing, or total behavioral problems or in substance use or substance use disorders. There was a marginally significant difference for NV males, compared with control males, to report more convictions for interpersonal violence (IR = 2.15; 95% CI 0.90 to 5.27; P = .082; data not shown). There were no overall NV-control differences in STIs, timing to first pregnancy, timing to first live birth, HIV risk, gang participation, counts of arrests, convictions, and arrests or convictions for interpersonal violence.

Estimates of Youth Substance-Use Disorders, Drug or Alcohol Use, STIs, Pregnancies, Births, Behavioral Problems, Arrests, Convictions, and Gang Activity

The estimates of intervention-control differences averaged over all other fixed classification variables, including those within subjects, and the same treatment effect was restricted to the group defined by females for arrest and conviction outcomes. This table shows the least-squares means at 18 y and repeated measures over time, which also are averaged over other fixed classification effects. For estimates of treatment effects based on repeated measures (labeled “all” under “age at assessment”), we assumed an error structure with different variances at each time for a given child and different covariance between pairs of times within each child. These were assumed to be the same for all children, and covariance between children was assumed to be negligible. Contrasts at specific, earlier time points are presented in earlier publications. 11 – 16   HR, hazards ratio; LS, least squares; SR, self-report; T2, prenatal transportation plus developmental screening and referral; T4, prenatal transportation plus developmental screening and referral and prenatal and infant and/or toddler nurse home visits.

Arrest-related outcomes were based on self-report combined with maternal and other-caregiver report (when available).

Survival rate at age 18 from Cox proportional hazard model.

Based on both self-report for all substances and urine assays for specific substances: phencyclidine, benzodiazepines, cocaine, amphetamines, tetrahydrocannabinol, opiates, and barbiturates.

Internalizing problems were based on youth self-report at ages 12 and 18 and maternal and/or other-caregiver report at child age 6, indicated by values exceeding the borderline or clinical threshold.

Externalizing and total problems were based on reports in which mothers and/or other caregivers (age 6); mothers and/or other caregivers, teachers, and children (2 of 3 at age 12); and youth and mothers and/or other caregivers (age 18) reported scores that exceeded the borderline or clinical threshold.

P < .10.

There were no overall intervention-control differences for any of the behavioral health outcomes, but the program improved the receptive language and math achievement of 18-year-olds born to mothers with LPR and, as a trend, reduced convictions among females. In addition, NV youth graduated with honors more frequently, and those born to mothers with LPR, compared with control-group counterparts, had better working memories and emotion-recognition skills and fewer disabilities leading to receipt of SSI. High rates of sample retention increase the validity of these findings.

The program effect on cognitive-related outcomes was, with the exception of graduation with honors, limited to youth born to mothers with LPR, conditional effects consistent with earlier phases of this trial, 11 – 15   and a subsequent trial. 18 – 20   Most of these effects, except for the twofold increase in graduation with honors overall and threefold reduction in SSI for disability in the LPR group, are small by conventional standards 45   but important because they reflect different aspects of cognition, disability, and academic success relevant to adult functioning. Moreover, most of these outcomes are based on directly measured abilities.

Program effects on emotion-recognition abilities and working memory, although not examined in other NFP trials, are consistent with a reduction in child maltreatment 46 , 47   and earlier program effects on maltreatment-related outcomes. 11 , 13 , 17   By age 2, for example, control children, compared with those visited by nurses, were hospitalized for fewer days for injuries and ingestions 11   ; all admissions for fractures and/or head trauma occurred in control infants born to mothers with LPR. 5 , 11   Control children born to mothers with LPR were less communicative and responsive to their mothers than those visited by nurses through 24 months 11   ; and through age 18, they exhibited more compromised development and achievement. 13 , 15   All preventable child mortality through age 20 occurred in the control group. 17  

The improvements in cognitive outcomes and reductions in disability in the LPR group at age 18 suggest that the intervention may lead to additional functional and societal savings for this group, including possible reductions in Alzheimer disease and related disorders, given their relationship with adolescent cognitive functioning. 48 , 49  

In interpreting the more pronounced program effect on the cognitive-related outcomes of children born to mothers with LPR, it is important to note that nurses visited the most vulnerable mothers more frequently, a consistent feature of NFP program design beginning with the Elmira trial. 26   Moreover, children born to mothers with LPR had greater room for improvement.

Note that families were not randomly assigned to different visitation schedules, so discerning the role of visit patterns on outcomes is challenging. In exploratory latent class analyses of home visits in the current trial, 3 patterns were uncovered: low attendance (33% of those visited), high attendance (48%), and increasing attendance (18%). Those in the low-visit group had the highest educations at baseline; those in the increasing group had low education, the lowest number of prenatal visits, and high rates of preterm delivery; and those in the high-visit group also had low education at baseline. These findings highlight the role that mothers, in addition to nurses, play in shaping visit attendance patterns and the methodologic challenges involved in estimating intervention effects for those with different visitation patterns. 27  

In using the visit patterns found here to guide community replication, 5   it is important to emphasize that the dosage metric that the NFP is designed to achieve is the one actually delivered in the original RCTs and that nurses adjusted visit frequency and content in an effort to ensure that they address specific risks and guide responsive caregiving in the most vulnerable subgroups.

The program effect on convictions among females, although a trend, is consistent with a corresponding finding in the Elmira trial, 10   reduced physical aggression among females at age 2 in the current trial, 50   and intervention effects on trajectories of externalizing problems in the subsequent Denver trial among females, but not males, at ages 2, 4, 6, and 9. 51   These female-limited beneficial effects may be connected to females’ particular susceptibility to the effects of prenatal stress on androgen activity during gestation 52   and hormone-dependent endpoints, including conduct disorder. 53   Moreover, females, compared with males, are particularly susceptible to the effects of harsh parenting on health, 54 , 55   including the development of aggression. 56  

There were no beneficial intervention effects in the current trial on 18-year-olds’ substance-use disorders, substance use, or internalizing disorders despite significant intervention effects in these domains at age 12 in the current trial 15   and on substance use, arrests, and convictions through age 15 in the Elmira trial. 9   The trend for NV males to report higher rates of conviction for interpersonal violence was not predicted. The absence of overall beneficial intervention effects on antisocial behavior at the end of adolescence, especially among males, is consistent with age-19 findings in the Elmira trial. 10   There are at least 2 possible explanations for this pattern of results.

The first is that NFP’s promotion of sensitive, responsive care and avoidance of harsh treatment may have decreased parents’ attention to setting effective limits, especially among noncompliant males. 56   The increase in NV males’ self-reported convictions for interpersonal violence found here, although not hypothesized, suggests that greater attention may be needed to address effective limit setting in NFP and to link this program to effective toddler and preschool parenting interventions. 57 – 60  

Second, the nearly normative rise in male adolescent–limited antisocial behavior not linked to maltreatment or early adversity 61   may mask intervention effects on life-course–persistent antisocial behavior linked to early maltreatment that may become evident once adolescents assume adult roles. Note, however, that adolescents who become ensnared in substance abuse and criminal activity are at risk for long-term criminal involvement. 61 – 63  

The current report has limitations. The first is that nearly all of the behavioral health findings were based on self-report, and some evidence suggests that NV women become more accurate reporters of socially undesirable behavior, such as smoking. 6   Although a case might also be made that NV youth were more attentive listeners and reporters (given program effects on verbal working memory), measurement of STIs and use of substances included urine assays, so this form of treatment-related report bias does not account for the absence of an intervention effect for these outcomes.

Second, we included 2 exploratory outcomes (high school graduation with honors and SSI for disability) that were not part of the original measurement design, so these findings need to be treated with caution.

Third, the age range for completing 18-year assessments was larger than anticipated but not different by treatment. Adjusting for youth age at assessment does not alter the findings (data not shown).

The fourth limitation is that the number of outcomes analyzed raises challenges with multiple comparisons. We have not adjusted for multiple comparisons in NFP trials. 64 – 67   We have focused instead on determining if findings replicate with different populations living in different contexts in separate trials. The long-term program effect on cognitive-related outcomes through the end of adolescence has not yet been tested in other trials, so particular caution is warranted in interpreting these outcomes.

This study found enduring program effects on the cognitive functioning of youth born to the mothers least capable of coping with the adversities that come with living in poverty and a trend for reduced convictions among females but no effects on other adolescent health behavior.

The current phase of this research was supported by the National Institute on Drug Abuse (1R01DA021624). Mr Knudtson had full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the analysis. We thank Evelyn Collins for tracing and engaging the study participants and managing the Memphis study office since 1991, Benjamin Jutson (University of Colorado Anschutz Medical Campus) for helping prepare this article, and Wendy Gehring (University of Colorado Anschutz Medical Campus) for her help with data management. Evelyn Collins, Benjamin Jutson, and Wendy Gehring received compensation from research grants for their work.

Deidentified individual participant data (including data dictionaries) will be made available in addition to study protocols, the statistical analysis plan, and the informed consent form. The data will be made available on publication to researchers who provide a methodologically sound proposal for use in achieving the goals of the approved proposal. Please contact Michael Knudtson, the study biostatistician, at [email protected] or 303-724-3199 for additional details.

Dr Olds conceptualized and designed the study, drafted the manuscript, and supervised the study; Dr Kitzman conceptualized and designed the study, obtained funding, and supervised the study; Dr Cole conceptualized and designed the study and obtained funding; Mr Knudtson acquired data and performed statistical analysis on the data; Dr Smith and Ms Anson acquired data; Drs Fishbein, DiClemente, Wingood, Caliendo, Hopfer, and Miller conceptualized and designed the study; and all authors analyzed and interpreted data, critically revised the manuscript for important intellectual content, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

This trial has been registered at www.clinicaltrials.gov (identifier NCT00708695).

FUNDING: Supported by the National Institutes of Health (research grant R01DA021624). Funded by the National Institutes of Health (NIH).

COMPANION PAPER: Companions to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2019-2606 and www.pediatrics.org/cgi/doi/10.1542/peds.2019-3889 .

adjusted odds ratio

maternal attitudes predictive of child abuse

confidence interval

effect size

incidence ratio

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Nurse-Family Partnership

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Competing Interests

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Model Profiles

Welcome baby.

Welcome Baby is a voluntary, universal hospital- and home-based intervention for families who are expecting or have recently given birth. Welcome Baby works with families to help them have healthy pregnancies and births; provide support during the postpartum period; maximize babies’ health and safety; build strong parent-child relationships; and facilitate access to support services when needed.

What is the model’s approach to providing home visiting services?

Welcome Baby provides a home visit before the 27th week of pregnancy, followed by a phone call check-in and a home visit after the 28th week of pregnancy. Families receive five home visits after their baby is born. A registered nurse makes the first visit 3 to 14 days postpartum. A parent coach then visits the family at 2 to 4 weeks, 2 months, 3 to 4 months, and 9 months postpartum. Services are provided until the child is 9 months old. Welcome Baby recommends families initiate services prenatally if their location permits, or at the time of birth in a participating hospital.

Welcome Baby serves all families with young children. Some local programs have specific eligibility requirements.

Who is implementing the model?

Home Visitors

Welcome Baby was implemented by 125 full-time equivalent (FTE) home visitors in 2022. The model requires at least a bachelor’s degree for home visitors. The maximum caseload requirement for home visitors is 100 families.

Supervisors

Welcome Baby was implemented by 25 FTE supervisors in 2022. The model recommends at least a bachelor’s degree for supervisors.

Where is the model implemented?

Welcome Baby operated in 14 local agencies in 1 state in 2022.

Families Served Through Home Visiting in 2022

<1% American Indian Alaska Native

<1% Native Hawaiian Pacific Islander

3% Multiple

79% Another race

78% Hispanic or Latino

22% Not Hispanic or Latino

Caregiver age

10% ≤21 years

40% 22-29 years

49% 30-44 years

<1% ≥45 years

Caregiver education

20% No HS diploma

37% HS diploma or GED

27% Some college or training

16% Bachelor's degree or higher

Household income

92% Low-income status

8% Not low-income status

Primary language

68% English

30% Spanish

2% Another language

0% 1-2 years

0% 3-5 years

Child insurance status

20% Private

Stay up to date on the latest home visiting information.

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Prenatal care: 3rd trimester visits

During the third trimester, prenatal care might include vaginal exams to check the baby's position.

Prenatal care is an important part of a healthy pregnancy, especially as your due date approaches. Your health care provider might ask you to schedule prenatal care appointments during your third trimester about every 2 or 4 weeks, depending on your health and pregnancy history. Starting at 36 weeks, you'll need weekly checkups until you deliver.

Repeat routine health checks

You'll be asked if you have any signs or symptoms, including contractions and leakage of fluid or bleeding. Your health care provider will check your blood pressure and weight gain, as well as your baby's heartbeat and movements.

Your health care provider might ask you to track of how often you feel the baby move on a daily basis — and to alert your health care team if the baby stops moving as much as usual.

Also, talk to your health care provider about any vaccinations you might need, including the flu shot and the tetanus toxoid, reduced diphtheria toxoid and acellular pertussis (Tdap) vaccine. Ideally, the Tdap vaccine should be given between 27 and 36 weeks of pregnancy.

Test for group B strep

Expect to be screened for group B streptococcus (GBS) during the third trimester. GBS is a common bacterium often carried in the intestines or lower genital tract that's usually harmless in adults. But babies who become infected with GBS from exposure during vaginal delivery can become seriously ill.

To screen for GBS , your health care provider will swab your lower vagina and anal area. The sample will be sent to a lab for testing. If the sample tests positive for GBS — or you previously gave birth to a baby who developed GBS disease — you'll be given intravenous antibiotics during labor. The antibiotics will help protect your baby from the bacterium.

Check the baby's position

Near the end of pregnancy, your health care provider might check to see if your baby is positioned headfirst in the uterus.

If your baby is positioned rump-first (frank breech) or feet-first (complete breech) after week 36 of pregnancy, it's unlikely that the baby will move to a headfirst position before labor. You might be able to have an external cephalic version. During this procedure, your health care provider will apply pressure to your abdomen and physically manipulate your baby to a headfirst position. This is typically done with ultrasound guidance by an experienced doctor. If you prefer not to have this procedure, or if your baby remains in a breech position, your health care provider will discuss planning a C-section delivery.

Keep asking questions

You will likely have plenty of questions as your due date approaches. Is it OK to have sex? How will I know when I'm in labor? What's the best way to manage the pain? Should I create a birth plan? Ask away! Feeling prepared can help calm your nerves before delivery.

Also, be sure to discuss signs that should cause you to call your health care provider, such as vaginal bleeding or fluid leaking from the vagina, as well as when and how to contact your health care provider once labor begins.

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• Frequently asked questions. Pregnancy FAQ079. If your baby is breech. American College of Obstetricians and Gynecologists. https://www.acog.org/Patients/FAQs/If-Your-Baby-Is-Breech. Accessed July 13, 2018.
• Prenatal care and tests. Office on Women's Health. https://www.womenshealth.gov/pregnancy/youre-pregnant-now-what/prenatal-care-and-tests. Accessed July 9, 2018.
• Cunningham FG, et al., eds. Prenatal care. In: Williams Obstetrics. 25th ed. New York, N.Y.: McGraw-Hill Education; 2018. https://www.accessmedicine.mhmedical.com. Accessed July 9, 2018.
• Hofmeyr GJ. External cephalic version. https://www.uptodate.com/contents/search. Accessed July 10, 2018.
• Lockwood CJ, et al. Prenatal care: Second and third trimesters. https://www.uptodate.com/contents/search. Accessed July 9, 2018.
• AskMayoExpert. Vaccination during pregnancy. Rochester, Minn.: Mayo Foundation for Medical Education and Research; 2018.

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• Pregnancy Support and Home Visiting

What Is Pregnancy Support Home Visiting?

Home visiting is a prevention strategy used to support pregnant moms and new parents to promote infant and child health, foster educational development and school readiness, and help prevent child abuse and neglect. Across the country, high-quality home visiting programs offer vital support to parents as they deal with the challenges of raising babies and young children. Participation in these programs is voluntary and families may choose to opt out whenever they want. Home visitors may be trained nurses, social workers or child development specialists. Their visits focus on linking pregnant women with prenatal care, promoting strong parent-child attachment, and coaching parents on learning activities that foster their child’s development and supporting parents’ role as their child’s first and most important teacher. Home visitors also conduct regular screenings to help parents identify possible health and developmental issues.

Legislators can play an important role in establishing effective home visiting policy in their states through legislation that can ensure that the state is investing in evidence-based home visiting models that demonstrate effectiveness, ensure accountability and address quality improvement measures. State legislation can also address home visiting as a critical component in states’ comprehensive early childhood systems.

What Does the Research Say?

Decades of research in neurobiology underscores the importance of children’s early experiences in laying the foundation for their growing brains. The quality of these early experiences shape brain development which impacts future social, cognitive and emotional competence. This research points to the value of parenting during a child’s early years. High-quality home visiting programs can improve outcomes for children and families, particularly those that face added challenges such as teen or single parenthood, maternal depression and lack of social and financial supports.

Rigorous evaluation of high-quality home visiting programs has also shown positive impact on reducing incidences of child abuse and neglect, improvement in birth outcomes such as decreased pre-term births and low-birthweight babies, improved school readiness for children and increased high school graduation rates for mothers participating in the program. Cost-benefit analyses show that high quality home visiting programs offer returns on investment ranging from $1.75 to $5.70 for every dollar spent due to reduced costs of child protection, K-12 special education and grade retention, and criminal justice expenses.

A review of prenatal home-visiting effectiveness for improving birth outcomes

Affiliation.

• 1 School of Public Health, University of Illinois at Chicago, Community Health Sciences Division, Chicago, IL, USA. [email protected]
• PMID: 21314710
• DOI: 10.1111/j.1552-6909.2011.01219.x

Objective: To determine the effectiveness of prenatal home visiting for improving prenatal care utilization and preventing preterm birth and low birth weight.

Data sources: Medline, the Cumulative Index to Nursing and Allied Health Literature, PsycINFO, and Social Work Abstract databases were searched for articles that examined prenatal home-visiting and prenatal care utilization or neonatal outcomes, with additional ascendancy and descendancy searches. Listservs were also used to identify unpublished evaluations.

Study selection: Quantitative studies meeting the following criteria were included in the analyses: published between 1985 and 2009, published in English, reported providing prenatal home visiting, and reported on prenatal care utilization or a neonatal outcome.

Data extraction: Study characteristics and findings related to prenatal care utilization, gestational age, and birth weight were abstracted independently by at least two authors. Study quality was assessed across five domains.

Data synthesis: The search yielded 28 studies comparing outcomes for women who did and did not receive prenatal home visiting, with 14 (59%) using an RCT design. Five (17%) studies reporting on prenatal care utilization found a statistically significant improvement in use of prenatal care for women with home visiting. Of 24 studies reporting an effect on birth outcomes, five (21%) found a significant positive effect on gestational age, and seven of 17 (41%) found a significant positive effect on birth weight.

Conclusions: More evidence suggests that prenatal home visiting may improve the use of prenatal care, whereas less evidence exists that it improves neonatal birth weight or gestational age. These findings have implications for implementing Title II of the Affordable Care Act.

© 2011 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.

Publication types

• Case Management*
• Community Health Nursing*
• Infant, Newborn
• Outcome Assessment, Health Care*
• Pregnancy Outcome
• Pregnancy, High-Risk
• Prenatal Care / statistics & numerical data*
• United States

Call or Text the Maternal Mental Health Hotline

Parents: don’t struggle alone

The National Maternal Mental Health Hotline provides free, confidential mental health support. Pregnant people, moms, and new parents can call or text any time, every day.

Start a call: 1-833-TLC-MAMA (1-833-852-6262)

Text now: 1-833-TLC-MAMA (1-833-852-6262)

Use TTY: Use your preferred relay service or dial 711 , then 1-833-852-6262 .

Learn more about the Hotline

• Programs & Impact

Maternal, Infant, and Early Childhood Home Visiting (MIECHV) Program

The MIECHV Program helps pregnant people and parents of young children improve health and well-being for themselves and their families. The Program does this by partnering trained home visitors with families to set and achieve goals. This work is part of our Early Childhood Systems programming .

Key summary documents

• Program Brief: Maternal, Infant, and Early Childhood Home Visiting Program (PDF - 320 KB)
• State Fact Sheets
• FY 2022 Home Visiting Infographic (PDF - 208 KB)

How does the Home Visiting Program help families?

Home visitors and families develop strong relationships and trust. They meet regularly to address families’ needs.

The Program aims to:

• Improve the overall health of mothers and children
• Get children ready to succeed in school
• Improve families’ economic well-being
• Connect families to other resources in their community (for example WIC , Medicaid, employment and educational resources, housing support, parenting support classes, and resources on how to stop smoking)

The Program works to prevent:

• Child injuries, abuse, and neglect
• Crime and domestic violence

What happens through home visiting?

Home visitors:

• Support healthy pregnancy habits
• Give advice on things like breastfeeding, making sure babies sleep safely, avoiding accidents with children, and eating well
• Show parents how to be positive and supportive with their children by reading, playing, and praising good behavior
• Encourage talking to babies and teaching them things from a young age
• Work with parents to plan for the future, continue their education, and find jobs and childcare
• Connect families to other services and resources in their community

How does the Program work?

Watch our video that explains this work.

HRSA and the Administration for Children and Families (ACF) fund states, jurisdictions, and tribes to develop and conduct home visiting programs. We provide funds to states and jurisdictions. ACF provides funds to tribes .

These programs must be based on evidence showing that they can meet the needs of families.

How do you ensure these programs work?

We use the Home Visiting Evidence of Effectiveness (HomVEE) review . ACF reviews home visiting program models to ensure they meet families’ needs.

There are 24 home visiting models that meet HomVEE and other eligibility criteria . States, jurisdictions, and tribes can select the best models for their communities.

How do you know how awardees are doing?

Awardees must report on how their program performs. The law requires them to do this across six benchmark areas, which include 19 performance measures (PDF - 137 KB) . They must show that they’ve improved in at least four of the six areas.

Do you offer to help awardees?

Yes. We want our awardees to succeed. Our program officers share their expertise to help improve the quality of the programs .

How is Home Visiting different from the Healthy Start program?

The Home Visiting Program and the Healthy Start program both reach pregnant women and families. But they’re different in terms of both funding and approach.

Funding differences

The Home Visiting Program awards grants to 50 states, the District of Columbia, and five territories to create state-wide networks that support and carry out HHS-approved evidence-based home visiting models.

Healthy Start provides direct funding to local entities . Healthy Start awardees serve communities in which babies die more often than the national average.

The 2023 funding increase for Home Visiting Program is the result of a five-year, bipartisan reauthorization of the Program by Congress. As such, this reauthorization further defines how the Home Visiting Program differs from Healthy Start.

Program differences

While both programs play a vital role in improving maternal and child health, they do so in distinct ways .

The Home Visiting Program:

• Preventing child abuse and neglect
• Promoting positive parenting
• Supporting school readiness
• Allows states to choose evidence-based models that fit their community’s needs

The Healthy Start program:

• Focuses on reducing infant deaths
• Providing both clinical and non-clinical health services
• Offering well-woman, maternity care, and doula services
• Helping with transportation and housing needs

And each program emphasizes different parts of the life course:

• The Healthy Start program focuses on the periods before, during, and after pregnancy.
• While some Home Visiting models focus on the time before birth, many models serve families throughout the early childhood period up until kindergarten.

Additional information

• FY 2022 MIECHV Program Reauthorization
• Demonstrating Improvement in the Maternal, Infant, and Early Childhood Home Visiting Program: A Report to Congress (PDF - 1 MB) (March 2016)

Past funding awards for home visiting programs

• FY 2023 Formula Funding Awards
• FY 2022 Formula Funding Awards
• FY 2021 American Rescue Plan Act Awards
• MIECHV Innovation Awards

Maternal, Infant, and Early Childhood Home Visiting (MIECHV) Program Notice of Funding Opportunity (PDF - 685 KB) *

*Note: Persons using assistive technology may not be able to fully access information in this file. For assistance, please email Rachel Herzfeldt-Kamprath or call 301-443-2524 .

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Dosage Effect of Prenatal Home Visiting on Pregnancy Outcomes in At-Risk, First-Time Mothers

Neera k. goyal.

Divisions of a Neonatology and Pulmonary Biology,

b Hospital Medicine,

c Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio

Eric S. Hall

d Biomedical Informatics,

Jareen K. Meinzen-Derr

e Biostatistics and Epidemiology,

Robert S. Kahn

f General Pediatrics, and

Jodie A. Short

Judith b. van ginkel, robert t. ammerman.

g Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio; and

BACKGROUND AND OBJECTIVE:

Home visiting programs seek to improve care management for women at high risk for preterm birth (<37 weeks). Our objective was to evaluate the effect of home visiting dosage on preterm birth and small for gestational age (SGA) infants.

Retrospective cohort study of women in southwest Ohio with a singleton pregnancy enrolled in home visiting before 26 weeks’ gestation. Vital statistics and hospital discharge data were linked with home visiting data from 2007 to 2010 to ascertain birth outcomes. Eligibility for home visiting required ≥1 of 4 risk factors: unmarried, low income, <18 years of age, or suboptimal prenatal care. Logistic regression tested the association of gestational age at enrollment and number of home visits before 26 weeks with preterm birth. Proportional hazards analysis tested the association of total number of home visits with SGA status.

Among 441 participants enrolled by 26 weeks, 10.9% delivered preterm; 17.9% of infants were born SGA. Mean gestational age at enrollment was 18.9 weeks; mean number of prenatal home visits was 8.2. In multivariable regression, ≥8 completed visits by 26 weeks compared with ≤3 visits was associated with an odds ratio 0.38 for preterm birth (95% confidence interval: 0.16–0.87), while having ≥12 total home visits compared with ≤3 visits was significantly associated with a hazards ratio 0.32 for SGA (95% confidence interval: 0.15–0.68).

CONCLUSIONS:

Among at-risk, first time mothers enrolled prenatally in home visiting, higher dosage of intervention is associated with reduced likelihood of adverse pregnancy outcomes.

Preterm birth (birth before 37 weeks’ gestation) is the single most challenging problem in modern obstetric practice and child health. The last 4 decades have seen a rise in preterm birth rates, with 12% of pregnancies, or 500 000 infants annually, delivering prematurely in the United States. 1 Decades of research demonstrate that this outcome occurs with profound sociodemographic disparities and is mediated by a complex matrix of biological, genetic, social, and environmental factors. 2 – 5

Home visiting is 1 strategy to improve maternal-child health outcomes through family education, training, and social support. 6 – 8 Recently, the Health Services and Resources Administration created the Maternal, Infant, and Early Childhood Home Visiting (MIECHV) program, with 1 aim to improve care management for pregnant women at high risk for preterm birth and low birth weight (<2500 g). 9 However, existing studies of home visiting and pregnancy outcomes have yielded inconsistent results, likely in part due to limitations in approach, lack of a theoretical framework specific to pregnancy outcomes, and variation in content and delivery of the intervention. 10 – 12

An important component of home visiting may be “dosage,” or duration of enrollment and intensity of participation. Evidence suggests that benefits of home visiting, measured on a range of outcomes, are affected by extent of exposure. 13 – 15 This may be particularly important for preterm birth, where modifiable risk factors such as nutrition, physical or mental health, and lifestyle behaviors may only be amenable to intervention if exposure begins early and is sustained at a sufficiently high intensity. 11 The goal of our study is to evaluate the effect of dosage of home visiting on pregnancy outcomes by using a regional perinatal data resource containing linked administrative and community-based program data. We hypothesized that, after adjustment for clinical, social, and demographic factors, higher number of prenatal visits in the first and second trimester are associated with a reduced likelihood of adverse pregnancy outcomes.

Study Design and Population

This was a retrospective, cohort study to examine the dosage effect of prenatal home visiting on singleton pregnancy outcomes in a population of at-risk, first-time mothers enrolled in an established, regional home visiting program, Every Child Succeeds (ECS), in southwest Ohio from 2007 to 2010.

ECS is a large community-based, home visiting program managed by Cincinnati Children’s Hospital Medical Center. Eligible mothers must have at least 1 of 4 risk characteristics: unmarried, low income (up to 300% of poverty level, receipt of Medicaid, or reported concerns about finances), <18 years of age, or suboptimal prenatal care. Participants are enrolled during pregnancy or before their child reaches 3 months of age. Home visits are provided by social workers, child development specialists, nurses, or paraprofessionals, starting with weekly or more-frequent visits and tapering to fewer visits as the child ages. ECS agencies in Ohio use the Healthy Families America model of home visiting; program goals are to (1) improve pregnancy outcomes through nutrition education and substance use reduction, (2) support parents in providing children with a safe, nurturing, and stimulating home environment, (3) optimize child health and development, (4) link families to health care and other services, and (5) promote economic self-sufficiency.

Referrals to the program may be self-initiated, or come from clinics, hospitals, and other community sources. To avoid inclusion of women who would not have had time for an intervention to influence their risk of delivering preterm, analyses were restricted to women enrolled before 26 weeks’ gestation. Women with multiple gestation pregnancies were also excluded from analysis because of their higher expected incidence of preterm birth.

Data Sources

ECS data were abstracted from a Web-based data entry system used to collect service provision data and for billing. This system contains detailed information on each participant, including enrollment timing by weeks of gestation, prenatal home visit history, and maternal demographic and psychosocial screening information. 16 Enrolled participants were consented to data being used for the purpose of quality assurance benchmarking and research.

These data were linked to Ohio vital statistics, available from the Ohio Department of Health, and birth-related hospital discharge of both mother and infant, available from the Ohio Hospital Association. Because there is no common unique identifier, record linkage was accomplished by using LINKS (University of Manitoba), an SAS-based probabilistic matching program (SAS Institute, Inc, Cary, NC). Selected variables used for linking included maternal and infant dates of birth, hospital of birth, delivery method, gender, and maternal address. Further details of linkage of data sources is described elsewhere by Hall et al. 17 The resulting data set provides information regarding maternal-child health, including demographics, social factors, pregnancy-related conditions, and infant outcomes. The Ohio Department of Health and Cincinnati Children’s Hospital Medical Center Institutional Review Boards approved this study.

Preterm birth was defined as infant birth before 37 weeks’ gestation; gestational age measures were obtained from vital statistics and represented the best clinical estimates. Because risk factors attributable to preterm birth may differ based on gestational age grouping (ie, 32–34 weeks’ vs 35–36 weeks’ gestation), we also repeated analyses with preterm birth specified as gestational age <35 weeks. 18 Our second key outcome was infant birth weight, also obtained from vital statistics, which was categorized as small for gestational age (SGA) versus appropriate or large for gestational age by using validated growth curves. 19

As a sensitivity analysis, we repeated evaluations by using a combined gestational age estimate from vital statistics rather than the clinical gestational age estimate, since previous studies have demonstrated discordance between these measures and potential unreliability of either one for population-based research. 20 The combined gestational age estimate incorporates the clinical estimate but primarily relies upon a calculated estimate based on maternal report of last menstrual period.

The primary predictor was dosage of prenatal home visiting (ie, the amount of intervention received among enrolled participants). To measure timing of onset, dates of enrollment were extracted from ECS and used to calculate gestational age at enrollment on the basis of the date of birth and gestational age at birth. Intensity of participation was measured as the number of completed prenatal home visits, also extracted from the ECS data system.

Although previous work has demonstrated an association between prenatal home visiting duration and preterm birth, 15 one potential concern may be that duration of prenatal enrollment is tautologically related to length of pregnancy and thus gestational age at delivery. Similarly, total number of completed prenatal home visits may reflect length of pregnancy, thereby limiting the ability to infer causality for preterm birth. To offset this concern, we used number of home visits before 26 weeks’ gestation for the preterm birth analysis, whereas for the SGA analysis we used total number of home visits. These variables were categorized into groupings of visit counts on the basis of reasonable cutoffs for ease of interpretation, as well as on the basis of distribution of the data.

As described by Hall et al, 17 data for maternal covariates were obtained through a combination of linked vital statistics, hospital discharge records, and home visiting data. These included race, ethnicity, payer source, maternal age, employment status, marital status, and education level. In addition to maternal BMI, calculated from vital statistics measures, indicator variables for relevant maternal comorbidities and obstetrical risk factors were constructed by using International Classification of Diseases, Ninth Revision, Clinical Modification codes and vital statistics data. These included chorioamnionitis, previous poor birth outcome (defined as previous fetal loss, stillbirth, or neonatal death), placental abnormalities, cervical abnormalities, hypertension/preeclampsia, anemia, diabetes, oligohydramnios, and premature rupture of membranes. Measures of tobacco, alcohol, and other substance use, as well as maternal living arrangement and frequency of contact with the infant’s father, were also obtained.

In addition to individual-level covariates, we measured the percent of residents living below poverty level by census tract for each participant’s geocoded address by using 5-year estimates from the 2010 American Community Survey, 21 as poverty has been demonstrated to be an important area-level measure associated with a range of health outcomes, including preterm birth. 3 , 22

Statistical Analysis

Preterm birth.

Bivariate analyses by using χ 2 or t tests were used to identify covariates associated with preterm birth. Factors deemed to be empirically or statistically important ( P < .25) were considered and tested in multiple logistic regression analyses by using step-wise multivariable modeling to derive parsimonious models. Models were tested for goodness of fit by using Akaike Information Criterion values and link tests for model specification. Multicollinearity was also assessed, with variance inflation factors for all retained variables < 10. 23

The independent association of home visiting exposure with SGA status was assessed by using a Cox proportional hazards survival model because of differences in timing to the outcome (birth) on the basis of gestational age at delivery. As above, bivariate analyses were used to identify covariates associated with the outcome; factors were then considered and tested in multivariable analyses by using step-wise multivariable modeling. Models were tested for goodness of fit and multicollinearity. Testing for violation of proportional hazards assumption was performed by using Schoenfeld residuals, which were not statistically significant.

All statistical tests were 2-sided, and type I error was controlled at 0.05. Final models were adjusted for clustering by home visiting agency by using robust SEs for cluster-correlated data. Analyses were performed by using Stata 11.0 (Stata Corp, College Station, TX).

From the data set representing 2330 women with linked home visiting records and Ohio birth certificates for the years 2007–2010, we identified 918 prenatally enrolled first-time mothers with a single gestation pregnancy. Of these, 441 enrolled by 26 weeks’ gestation and were included in the final analytic sample. The preterm birth rate among the sample was 10.9%, and 17.9% of infants were born SGA. Sixty-one percent were African American and 32% were white, 98% were unmarried, 53% had not completed high school, mean maternal age was 20 years, and 84% were insured through Medicaid. Mean gestational age at enrollment was 18.9 weeks, and number of total completed prenatal home visits ranged from 1 to 26 visits. As expected, there were fewer visits completed before 26 weeks’ gestation, ranging from 1 to 16.

Bivariate Comparisons

Table 1 depicts comparisons of key predictors and covariates with preterm birth. In bivariate analyses, preterm birth was not significantly associated with gestational age at enrollment or categorized number of home visits before 26 weeks. A higher percentage of women delivering preterm had a history of previous poor pregnancy outcome (25.0% vs 10.7%), hypertension/preeclampsia (27.1% vs 12.0%), and disorders of placentation (4.2% vs 0.5%) compared with women delivering at full term gestations, all P < .05.

Clinical and Demographic Characteristics of Mothers Enrolled in Home Visiting Prenatally With and Without Preterm Birth

As shown in Table 2 , bivariate comparisons demonstrated that women delivering an SGA infant were not significantly different from women without SGA infants in mean gestational age at enrollment or categorized number of total prenatal home visits. A higher percentage of women delivering an SGA infant had a history of cigarette use (46.8% vs 31.5%) and other drug use (19.0% vs 10.8%) compared with women delivering non-SGA infants, P < .05. Mean maternal BMI was significantly higher in the group with SGA infants (25.7 vs 23.9, P < .01).

Clinical and Demographic Characteristics of Mothers Enrolled in Home Visiting Prenatally With and Without SGA infants

Multivariable Analyses

As shown in Table 3 , timing of enrollment in home visiting was not independently associated with preterm birth. However, number of home visits before 26 weeks was statistically significant; compared with the reference group of ≤3 home visits, completion of 8 or more home visits by 26 weeks was associated with an adjusted odds ratio (aOR) 0.38 for preterm birth (95% confidence interval [CI]: 0.16–0.87). This association was robust to re-specification of the outcome as delivery before 35 weeks’ gestation. Several maternal covariates were associated with a significantly increased AOR of preterm birth, including hypertension/preeclampsia (AOR, 2.99 [95% CI: 1.66–5.41]), previous poor pregnancy outcome (2.87 [95% CI: 1.52–5.44]), and placental disorders (6.77 [95% CI: 1.58–29.0]).

Multivariable Logistic Regression of Predictors With Preterm Birth, AORs

Overall models and coefficients for key predictors did not change significantly when combined versus clinical gestational age estimates were used; therefore, these data are not shown.

Table 4 depicts results of the multivariable proportional hazards analysis for SGA. After adjustment for all covariates, receipt of ≥12 prenatal home visits compared with the reference group of 1 to 3 prenatal home visits was significantly associated with a 0.32 hazard ratio (HR) of SGA status (95% CI: 0.15–0.68). This association was not detected for categories with fewer numbers of total prenatal home visits (ie, 4–7 visits or 8–11 visits). Maternal age <18 years was significantly associated with SGA status compared with age >18 years (HR: 1.37 [95% CI: 1.06–1.76]), as was maternal race classified as Other compared with white (HR: 3.06 [95% CI: 1.13–8.25]).

Multivariable Cox Proportional Hazards Analysis of Predictors With SGA Status, Adjusted HRs

Although the effectiveness of home visiting has been demonstrated for many outcomes, including child abuse, infant development, and parenting, the impact of this intervention for pregnancy outcomes is currently not well understood. 6 – 8 , 10 , 11 , 24 Given the importance of preterm birth to pediatric morbidity and health care spending, as well as the investment of federal funding in home visiting through MIECHV, further conceptualization and measurement of prenatal delivery of home visiting is critical to a comprehensive understanding of the utility and potential cost benefits of this intervention. The current study is a retrospective analysis of dosage of home visiting and singleton pregnancy outcomes in a regional population of at-risk, first time mothers. Results demonstrate that a significant reduction in the likelihood of preterm birth and SGA status is associated with receipt of the highest number of prenatal visits compared with women receiving the lowest number of visits.

This study builds on previous work evaluating the impact of ECS on infant outcomes at a population level, in which program enrollment as a dichotomous predictor was not associated with differences in infant gestational age. 25 Although many previous randomized controlled trials and quasi-experimental studies of community-based programs have revealed promise in improving preterm birth and infant birth weight, 26 – 31 several reviews of the existing body of literature have demonstrated an overall lack of consistent evidence to support the effectiveness of prenatal home visiting. 10 , 11 , 24 , 32 , 33 One cause for the overall low percent of positive findings may be lack of attention to dosage of the intervention (ie, the number or duration of home visits per participant). 11 , 15 Given the complexity of biological, genetic, social, and environmental factors influencing preterm birth, any intervention addressing modifiable risks like nutrition or health behaviors would seem to require adequate intensity and length of exposure to be effective. The importance of home visiting dosage has already been shown for other outcome domains, including child behavioral problems and maternal parenting. 13 , 14

Strengths of the current study include minimized selection bias associated with enrollment among an at-risk population by constraining analyses to women enrolled in home visiting prenatally. Furthermore, the sample was restricted to those enrolled before 26 weeks’ gestation to maximize the likelihood that participants had time for the intervention to influence their pregnancy. Rather than focus on total number of visits or duration of prenatal participation, which present a problem of tautology with regards to length of pregnancy and thus preterm birth, we measured only visits before 26 weeks for the preterm birth analysis, which is also consistent with our hypothesis that early intervention at a high intensity is required to address modifiable risk factors for preterm birth. An additional strength is our application of linked data systems to support policy relevant research in a “real world” setting, which may be particularly important given the requirement for evaluation of home visiting within existing, established programs as services are expanded.

There are several limitations related to use of administrative data in this retrospective analysis. Complications and comorbidities identified by using vital statistics and hospital discharge data may be undercoded or overcoded, resulting in a misclassification bias. 34 – 36 Another limitation may be generalizability of findings given the sample size and regional population represented. Although we attempted to maintain parsimonious regression models, the number of preterm cases is small relative to the number of retained covariates. Although the ECS prenatal curriculum provides guidance to home visitors for standardized content on the basis of the week of pregnancy, another limitation of this study is potential variation in the content of home visits across participants in this cohort, which is not included in analyses. Lastly, an important limitation of any observational study is the inability to infer causality from observed associations; differences in maternal risk because of nonrandom assignment into groups of higher and lower dosage of home visiting may in part account for the lack of significant association between visit number and outcomes in bivariate analyses. Although we attempted to minimize bias through study design and inclusion of important covariates in multivariate analyses, the extent to which findings were attributable to unmeasured confounding (ie, maternal motivation or self-efficacy) is unclear. This concern may be partially addressed by further omission of subgroups potentially contributing to bias from the analysis; as an example, when alcohol and other substance users ( n = 64) are omitted, the aOR of preterm birth associated with highest number of prenatal visits before 26 weeks remains statistically significant (0.35 [95% CI 0.19–0.64]).

Conclusions

Given the contribution of birth outcomes to pediatric morbidity and health care spending, as well as the federal investment in home visiting through MIECHV, further conceptualization and measurement of prenatal delivery of home visiting is critical to a comprehensive understanding of the utility and potential cost benefits of this intervention. Current findings support that high dosage of prenatal home visits is associated with reduced odds of preterm birth or SGA status. As programs expand services within at-risk populations, enrollment early in pregnancy and promotion of high levels of engagement in the first and second trimester may be important to achieving measurable benefits of this intervention.

Acknolwedgments

The authors acknowledge David Jones for assisting with the census tract-level data and Dr Louis Muglia, Dr James Greenberg, and Dr Rita Pickler for critical review of the article. We acknowledge the participation and support of the United Way of Greater Cincinnati, Kentucky HANDS, and Ohio Help Me Grow.

Dr Goyal conceptualized and designed the study, performed statistical analysis, and drafted the initial manuscript; Dr Hall coordinated and supervised administrative data collection and data linkages and reviewed and revised the manuscript; Dr Meinzen-Derr assisted with study design, supervised all statistical analysis, and reviewed and revised the manuscript; Dr Kahn assisted with design of the study and interpretation of the data and reviewed and revised the manuscript; Ms Short coordinated data collection for the home visiting program, assisted with interpretation of the data, and critically reviewed the manuscript; Dr Van Ginkel supervised data collection for the home visiting program and critically reviewed the manuscript; Dr Ammerman supervised the conceptualization of the study and designed the study, supervised interpretation of the data, and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted.

Dr Goyal's involvement in this project was supported by the Building Interdisciplinary Research Careers in Women's Health (BIRCWH) program, co-funded by the Office of Research on Women's Health (ORWH) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), Award Number 5K12HD051953-07. Dr Hall's and Dr Meinzen-Derr's participation was supported by the Place Outcomes Award from Cincinnati Children's Hospital Medical Center. Dr Ammerman was supported by Grant R01MH087499 from the National Institute of Mental Health (NIMH).

The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of Mental Health, or the National Institutes of Health.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

FUNDING: Dr Goyal’s involvement in this project was supported by the Building Interdisciplinary Research Careers in Women’s Health program, cofunded by the Office of Research on Women’s Health and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, award number 5K12HD051953-07. Dr Hall’s and Dr Meinzen-Derr’s participation was supported by the Place Outcomes Award from Cincinnati Children’s Hospital Medical Center. Dr Ammerman was supported by grant R01MH087499 from the National Institute of Mental Health. Funded by the National Institutes of Health (NIH).

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.