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Nurse Family Partnership

Public Health & Prevention: Home- or Family-based
Benefit-cost methods last updated December 2017.  Literature review updated March 2018.
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The Nurse Family Partnership program provides intensive visitation by nurses during a woman’s pregnancy and the first two years after birth. The program is designed to serve low-income, at-risk pregnant women expecting their first child. The goal is to promote the child's development and provide support and instructive parenting skills to parents. Among programs included in the meta-analysis, participants received 25–35 home visits on average, spread over approximately two years.
BENEFIT-COST
META-ANALYSIS
CITATIONS
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2016). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $3,027 Benefits minus costs $1,827
Participants $8,905 Benefit to cost ratio $1.15
Others $774 Chance the program will produce
Indirect $940 benefits greater than the costs 53 %
Total benefits $13,646
Net program cost ($11,819)
Benefits minus cost $1,827
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Benefits from changes to:1 Benefits to:
Taxpayers Participants Others2 Indirect3 Total
Crime $185 $0 $305 $93 $582
Labor market earnings associated with employment $512 $1,127 $0 $0 $1,639
Labor market earnings associated with major depression ($157) ($346) $0 ($4) ($507)
Health care associated with major depression ($48) ($16) ($59) ($23) ($146)
Public assistance $455 ($193) $0 $229 $490
Health care associated with anxiety disorder $21 $7 $27 $11 $66
Food assistance $234 ($212) $0 $118 $141
Subtotals $1,202 $367 $272 $424 $2,265
From secondary participant
Crime $258 $0 $552 $131 $941
Child abuse and neglect $137 $1,534 $0 $68 $1,739
Out-of-home placement ($1,400) $0 $0 ($694) ($2,094)
K-12 grade repetition ($49) $0 $0 ($24) ($73)
K-12 special education ($214) $0 $0 ($105) ($319)
Property loss associated with alcohol abuse or dependence $0 $1 $3 $0 $4
Labor market earnings associated with child abuse & neglect $3,254 $7,165 $0 $0 $10,419
Costs of higher education ($192) ($162) ($53) ($96) ($503)
Infant mortality $0 $0 $0 $7,112 $7,112
Health care associated with low birthweight births ($63) $0 $0 ($31) ($94)
Health care associated with very low birthweight births $94 $0 $0 $47 $141
Subtotals $1,825 $8,538 $501 $6,408 $17,273
Adjustment for deadweight cost of program $0 $0 $0 ($5,892) ($5,892)
Totals $3,027 $8,905 $774 $940 $13,646
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $5,944 2015 Present value of net program costs (in 2016 dollars) ($11,819)
Comparison costs $0 2015 Cost range (+ or -) 10 %
Treatment cost estimates for this program reflect costs beyond treatment as usual. The annual per-participant cost estimate is based on average total cost per family in Washington State, provided by Siobhan Mahorter at the Nurse Family Partnership National Service Office, January 2017.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Estimated Cumulative Net Benefits Over Time (Non-Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in non-discounted dollars to simplify the “break-even” point from a budgeting perspective. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

^WSIPP’s benefit-cost model does not monetize this outcome.

^^WSIPP does not include this outcome when conducting benefit-cost analysis for this program.

***We report this outcome twice: once for mothers (designated as the primary participant) and once for infants (designated as the secondary participant). We do this because the outcome is associated with costs and benefits for both mothers and infants, and the amount of the cost or benefit is different for mothers than it is for infants.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Treatment Age Primary or secondary participant No. of effect sizes Treatment N Adjusted effect sizes (ES) and standard errors (SE) used in the benefit-cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
High school graduation^^ 19 Primary 2 401 0.035 0.086 23 0.035 0.086 23 0.096 0.271
Low birthweight births (< 2500g)*** 19 Primary 3 9617 0.021 0.022 19 0.000 0.000 20 0.022 0.325
Very low birthweight birth (< 1500g)*** 19 Primary 2 9162 -0.040 0.045 19 0.000 0.000 20 -0.040 0.373
Preterm birth (< 37 weeks)*** 19 Primary 3 9617 0.021 0.027 19 0.000 0.000 20 0.001 0.990
Alcohol use^ 19 Primary 2 401 0.088 0.214 23 0.088 0.214 33 0.264 0.186
Substance misuse^ 19 Primary 1 183 0.013 0.119 37 0.013 0.119 47 0.035 0.831
Cannabis use^ 19 Primary 2 401 -0.061 0.108 23 -0.061 0.108 33 -0.168 0.134
Smoking during late pregnancy^ 19 Primary 1 237 -0.099 0.114 19 n/a n/a n/a -0.099 0.386
Substance use^ 19 Primary 2 266 -0.049 0.094 31 -0.049 0.094 41 -0.237 0.215
Attention-deficit/hyperactivity disorder symptoms 1 Secondary 1 166 -0.094 0.142 10 0.000 0.008 11 -0.260 0.068
NICU admission 1 Secondary 1 564 -0.016 0.056 1 0.000 0.000 2 -0.016 0.781
High school graduation 1 Secondary 2 226 0.023 0.107 18 0.023 0.107 18 0.051 0.682
Test scores 1 Secondary 3 370 0.026 0.066 14 0.023 0.073 17 0.060 0.492
Smoking before end of middle school 1 Secondary 1 191 -0.075 0.087 13 -0.075 0.087 23 -0.208 0.018
Cannabis use before end of middle school 1 Secondary 1 191 -0.114 0.087 13 -0.114 0.087 23 -0.317 0.001
Alcohol use in high school^^ 1 Secondary 1 38 -0.162 0.206 16 -0.162 0.206 26 -0.450 0.031
Smoking in high school^^ 1 Secondary 1 38 -0.122 0.206 16 -0.122 0.206 26 -0.339 0.102
Substance use^ 1 Secondary 1 194 0.013 0.115 19 0.013 0.115 29 0.035 0.752
Substance misuse^ 1 Secondary 1 194 0.045 0.115 19 0.045 0.115 29 0.126 0.274
Emergency department visits^ 1 Secondary 1 216 -0.010 0.105 3 0.000 0.086 5 -0.027 0.796
Externalizing behavior symptoms 1 Secondary 2 319 0.010 0.094 15 0.005 0.049 18 -0.065 0.676
Hospitalization^^ 1 Secondary 2 5232 0.038 0.021 4 0.000 0.000 5 -0.018 0.832
Illicit drug use in high school^^ 1 Secondary 1 38 -0.087 0.206 16 -0.087 0.206 26 -0.242 0.242
Internalizing symptoms 1 Secondary 2 332 -0.048 0.092 15 -0.035 0.073 17 -0.174 0.127
Preterm birth (< 37 weeks)*** 1 Secondary 3 9617 0.021 0.027 1 0.000 0.000 2 0.001 0.990
Small for gestational age (SGA)^ 1 Secondary 1 455 0.000 0.081 1 0.000 0.000 2 0.000 1.000
Preschool test scores^ 1 Secondary 2 394 0.043 0.065 5 0.013 0.071 17 0.120 0.086
Very preterm birth (< 30 weeks)^ 1 Secondary 1 8598 -0.135 0.079 1 0.000 0.000 2 -0.135 0.087
Anxiety disorder 19 Primary 1 192 -0.013 0.116 37 -0.007 0.142 39 -0.037 0.833
Crime 19 Primary 2 229 -0.106 0.080 31 -0.106 0.080 41 -0.463 0.092
Employment 19 Primary 3 426 0.019 0.062 27 0.000 0.000 28 0.078 0.405
Food assistance 19 Primary 3 450 -0.031 0.060 30 -0.031 0.060 30 -0.130 0.264
Major depressive disorder 19 Primary 1 192 0.025 0.116 37 0.013 0.142 39 0.069 0.711
Public assistance 19 Primary 3 456 -0.036 0.060 30 -0.036 0.060 30 -0.153 0.176
Alcohol use before end of middle school 1 Secondary 1 191 -0.114 0.087 13 -0.114 0.087 23 -0.317 0.001
Child abuse and neglect 1 Secondary 2 206 -0.353 0.141 6 -0.353 0.141 17 -0.620 0.010
Crime 1 Secondary 2 232 -0.032 0.080 18 -0.032 0.080 28 -0.119 0.320
Infant mortality 1 Secondary 2 8815 -0.130 0.118 1 0.000 0.000 2 -0.143 0.170
K-12 grade repetition 1 Secondary 3 367 0.051 0.094 11 0.051 0.094 11 0.127 0.317
K-12 special education 1 Secondary 3 367 0.031 0.111 11 0.031 0.111 11 0.046 0.820
Low birthweight births (< 2500g)*** 1 Secondary 3 9617 0.021 0.022 1 0.000 0.000 2 0.022 0.325
Out-of-home placement 1 Secondary 1 191 0.147 0.116 13 0.147 0.116 17 0.408 0.001
Very low birthweight birth (< 1500g)*** 1 Secondary 2 9162 -0.040 0.045 1 0.000 0.000 2 -0.040 0.373

Citations Used in the Meta-Analysis

Carabin, H., Cowan, L.D., Beebe, L.A., Skaggs, V.J., Thompson, D., & Agbangla, C. (2005). Does participation in a nurse visitation programme reduce the frequency of adverse perinatal outcomes in first-time mothers? Paediatric and Perinatal Epidemiology, 19(3), 194-205.

Holmes & Rutledge (2016). Evaluation of the Nurse Family Partnership in North Carolina. UNC Gillings School of Global Public Health.

Kitzman, H., Olds, D.L., Henderson Jr, C.R., Hanks, C., Cole, R., Tatelbaum, R., . . . Engelhardt, K. (1997). Effect of prenatal and infancy home visitation by nurses on pregnancy outcomes, childhood injuries, and repeated childbearing: A randomized controlled trial. JAMA, 278(8), 644-652.

Kitzman, H.J., Olds, D.L., Cole, R.E., Hanks, C.A., Anson, E.A., Arcoleo, K.J., . . . Holmberg, J.R. (2010). Enduring effects of prenatal and infancy home visiting by nurses on children: Follow-up of a randomized trial among children at age 12 years. Archives of Pediatrics & Adolescent Medicine, 164(5), 412-418.

Matone, M., O'Reilly, A.L., Luan, X., Localio, A.R., & Rubin, D.M. (2012). Emergency department visits and hospitalizations for injuries among infants and children following statewide implementation of a home visitation model. Maternal and Child Health Journal, 16(9), 1754-61.

Mejdoubi, J., van, . H. S. C. C. M., van, L. F. J. M., Crone, M., Crijnen, A., HiraSing, R. A., & Special Sections: Focus on Infant Feeding and Postnatal Health and Well-being. (2014). Effects of nurse home visitation on cigarette smoking, pregnancy outcomes and breastfeeding: A randomized controlled trial. Midwifery, 30(6), 688-695.

Mejdoubi, J., van, . H. S. C. C. M., van, L. F. J. M., Heymans, M. W., Crijnen, A., Hirasing, R. A., & Carlo, W. A. (2015). The Effect of VoorZorg, the Dutch Nurse-Family Partnership, on Child Maltreatment and Development: A Randomized Controlled Trial. Plos One, 10(4).

Olds, D., Henderson, C.R., Cole, R., Eckenrode, J., Kitzman, H., Luckey, D. (1998). Long-term effects of nurse home visitation on children's criminal and antisocial behavior: 15-year follow-up of a randomized controlled trial. JAMA, 280(14), 1238-1244.

Olds, D.L., Eckenrode, J., Henderson, C.R., Jr., Kitzman, H., Powers, J., Cole, R., . . . Luckey, D. (1997). Long-term effects of home visitation on maternal life course and child abuse and neglect: Fifteen-year follow-up of a randomized trial. JAMA, 278(8), 637-643.

Olds, D.L., Holmberg, J.R., Donelan-McCall, N., Luckey, D.W., Knudtson, M.D., & Robinson, J. (2014). Effects of home visits by paraprofessionals and by nurses on children: follow-up of a randomized trial at ages 6 and 9 years. JAMA Pediatrics, 168(2), 114-21.

Olds, D.L., Kitzman, H., Cole, R., Robinson, J., Sidora, K., Luckey, D.W., . . . Holmberg, J. (2004). Effects of nurse home- visiting on maternal life course and child development: Age 6 follow-up results of a randomized trial. Pediatrics, 114(6), 1550-1559.

Olds, D.L., Kitzman, H., Hanks, C., Cole, R., Anson, E., Sidora-Arcoleo, K., . . . Bondy, J. (2007). Effects of nurse home visiting on maternal and child functioning: Age-9 follow-up of a randomized trial. Pediatrics, 120(4), 832-845.

Olds, D.L., Kitzman, H., Knudtson, M., Cole, R., Miller, T., Fishbein, D., . . . Collins, E. (2016). Age-18 follow-up of home visiting intervention (Grant #R01DA021624).

Olds, D.L., Kitzman, H., Knudtson, M.D., Anson, E., Smith, J.A., & Cole, R. (2014). Effect of home visiting by nurses on maternal and child mortality: results of a 2-decade follow-up of a randomized clinical trial. JAMA Pediatrics, 168(9), 800-806.

Olds, D L., Kitzman, H.J., Cole, R.E., Hanks, C.A., Arcoleo, K.J., Anson, E.A., . . . Stevenson, A. (2010). Enduring effects of prenatal and infancy home visiting by nurses on maternal life course and government spending: Follow-up of a randomized trial among children at age 12 years. Archives of Pediatrics & Adolescent Medicine, 164(5), 419-424.

Olds, D.L., Robinson, J., O'Brien, R., Luckey, D.W., Pettitt, L.M., Henderson, C.R., Jr., . . . Talmi, A. (2002). Home visiting by paraprofessionals and by nurses: A randomized, controlled trial. Pediatrics, 110(3), 486-496.

Olds, D.L., Robinson, J., Pettitt, L., Luckey, D. W., Holmberg, J., Ng, R.K., . . . Henderson, C.R., Jr. (2004). Effects of home visits by paraprofessionals and by nurses: Age 4 follow-up results of a randomized trial. Pediatrics, 114(6), 1560-1568.

Rubin, D.M., O'Reilly, A.L., Luan, X., Dai, D., Localio, A.R., & Christian, C.W. (2011). Variation in pregnancy outcomes following statewide implementation of a prenatal home visitation program. Archives of Pediatrics & Adolescent Medicine, 165,(3), 198-204.