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Interventions to prevent excessive gestational weight gain (population with obesity-related risk factors)

Health Care: Maternal and Infant Health
Benefit-cost estimates updated May 2017.  Literature review updated December 2016.
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A wide range of programs aim to prevent excessive gestational weight gain in a population with obesity-related risk factors (based on their pre-pregnancy BMI). We included programs that offer an exercise class and programs that offer counseling on recommended weight gain during pregnancy. Typically athletic trainers lead exercise programs in groups and counseling is delivered one-on-one in a clinical setting by a health educator, midwife, psychologist, or obstetrician.
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 ($212) Benefits minus costs ($953)
Participants ($17) Benefit to cost ratio ($3.71)
Others ($215) Chance the program will produce
Indirect ($307) benefits greater than the costs 47 %
Total benefits ($751)
Net program cost ($202)
Benefits minus cost ($953)
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
Health care associated with general hospitalization $49 $3 $42 $24 $118
Health care associated with low birthweight births ($41) ($2) ($41) ($21) ($104)
Subtotals $8 $1 $1 $4 $14
From secondary participant
Infant mortality ($4) ($9) $0 ($99) ($113)
Health care associated with preterm births $148 $6 $148 $74 $375
Health care associated with low birthweight births ($363) ($15) ($363) ($184) ($926)
Subtotals ($220) ($18) ($216) ($209) ($663)
Adjustment for deadweight cost of program $0 $0 $0 ($101) ($101)
Totals ($212) ($17) ($215) ($307) ($751)
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $200 2015 Present value of net program costs (in 2016 dollars) ($202)
Comparison costs $0 2015 Cost range (+ or -) 50 %
These interventions varied in length, from a single session to seven months. The per-participant cost was calculated by multiplying the number of staff hours per participant by the average 2015 salary of the staff member as reported by the Bureau of Labor Statistics (http://www.bls.gov/oes/current/oes_wa.htm#29-0000). We multiplied the average salary by 1.441 to estimate the total staff costs including benefits.
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.

***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 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
Blood pressure^ Primary 6 1641 -0.038 0.070 30 0.000 0.000 31 -0.038 0.584
Cesarean sections Primary 8 2525 -0.063 0.058 30 0.000 0.000 31 -0.063 0.276
Excess gestational weight gain^ Primary 10 1846 -0.240 0.110 30 0.000 0.000 31 -0.240 0.029
Gestational diabetes^ Primary 13 2906 -0.124 0.074 30 0.000 0.000 31 -0.124 0.093
Hospitalization Primary 1 1075 -0.034 0.041 30 0.000 0.000 31 -0.034 0.398
Low birthweight births*** Primary 3 1101 0.252 0.209 30 0.000 0.000 31 0.252 0.230
Postpartum depression^ Primary 2 134 0.000 0.285 30 0.000 0.000 31 0.000 1.000
Preeclampsia^ Primary 5 2432 0.019 0.076 30 0.000 0.000 31 0.019 0.805
Preterm birth (< 37 weeks)*** Primary 4 2016 -0.132 0.087 30 0.000 0.000 31 -0.132 0.132
Small for gestational age (SGA)*** Primary 1 124 0.190 0.267 30 0.000 0.000 31 0.190 0.476
Infant mortality Secondary 1 783 0.001 0.221 1 0.000 0.000 2 0.001 0.997
Low birthweight births*** Secondary 3 1101 0.252 0.209 1 0.000 0.000 2 0.252 0.230
Macrosomia (birth weight > 4000g)***^ Secondary 7 2565 -0.118 0.053 1 0.000 0.000 2 -0.118 0.026
NICU admission Secondary 3 1891 -0.005 0.050 1 0.000 0.000 2 -0.005 0.926
Preterm birth (< 37 weeks)*** Secondary 4 2016 -0.132 0.087 1 0.000 0.000 2 -0.132 0.132
Small for gestational age (SGA)*** Secondary 1 124 0.190 0.267 1 0.000 0.000 2 0.190 0.476

Citations Used in the Meta-Analysis

Bogaerts, A.F., Devlieger, R., Nuyts, E., Witters, I., Gyselaers, W., & Van den Bergh, B.R. (2013). Effects of lifestyle intervention in obese pregnant women on gestational weight gain and mental health: a randomized controlled trial. International Journal of Obesity, 37(6), 814-21.

Dodd, J.M., Turnbull, D., McPhee, A.J., Deussen, A.R., Grivell, R.M., Yelland, L.N., . . . Robinson, J.S. (2014). Antenatal lifestyle advice for women who are overweight or obese. Obstetrical & Gynecological Survey, 69(6), 311-313.

Harrison, C.L., Lombard, C.B., Strauss, B.J., & Teede, H.J. (2013). Optimizing healthy gestational weight gain in women at high risk of gestational diabetes: a randomized controlled trial. Obesity, 21(5), 904-909.

Hawkins, M., Hosker, M., Marcus, B.H., Rosal, M.C., Braun, B., Stanek, E.J., . . . Chasan-Taber, L. (2015). A pregnancy lifestyle intervention to prevent gestational diabetes risk factors in overweight Hispanic women: a feasibility randomized controlled trial. Diabetic Medicine, 32(1), 108-15.

Luoto, R., Kinnunen, T.I., Aittasalo, M., Kolu, P., Raitanen, J., Ojala, K., Mansikkamäki, K., . . . Tulokas, S. (2011). Primary prevention of gestational diabetes mellitus and large-for-gestational-age newborns by lifestyle counseling: A cluster-randomized controlled trial. Plos Medicine, 8(5), e1001036.

Nobles, C., Marcus, B.H., Stanek, E.J., Braun, B., Whitcomb, B.W., Solomon, C.G., . . . Chasan-Taber, L. (2015). Effect of an exercise intervention on gestational diabetes mellitus: a randomized controlled trial. Obstetrics and Gynecology, 125(5), 1195-204.

Oostdam, N., van Poppel, M.N.M., Wouters, M.G.A.J., Eekhoff, E.M.W., Bekedam, D.J., Kuchenbecker, W.K.H., . . . Mechelen, W. van. (2012). No effect of the FitFor2 exercise programme on blood glucose, insulin sensitivity, and 32 birthweight in pregnant women who were overweight and at risk for gestational diabetes: Results of a randomised controlled trial. BJOG: An International Journal of Obstetrics & Gynaecology, 119, 1098-1107.

Poston, L., Briley, A.L., Barr, S., Bell, R., Croker, H., Coxon, K., . . . Sandall, J. (2013). Developing a complex intervention for diet and activity behaviour change in obese pregnant women (the UPBEAT trial); assessment of behavioural change and process evaluation in a pilot randomised controlled trial. BMC Pregnancy and Childbirth, 13(1) 148- 164.

Poston, L., Bell, R., Croker, H., Flynn, A.C., Godfrey, K.M., Goff, L., . . . Briley, A. (2015). Effect of a behavioural intervention in obese pregnant women (the UPBEAT study): a multicentre, randomised controlled trial. The Lancet. Diabetes & Endocrinology, 3(10), 767-777.

Quinlivan, J.A., Lam, L.T., & Fisher, J. (2011). A randomised trial of a four-step multidisciplinary approach to the antenatal care of obese pregnant women. Australian and New Zealand Journal of Obstetrics and Gynecology, 51(2), 141- 146.

Renault, K.M., Norgaard, K., Nilas, L., Carlsen, E.M., Cortes, D., Pryds, O., & Secher, N.J. (2014). The Treatment of Obese Pregnant Women (TOP) study: a randomized controlled trial of the effect of physical activity intervention assessed by pedometer with or without dietary intervention in obese pregnant women. American Journal of Obstetrics and Gynecology, 210(2), 134.el-9.

Thornton, Y.S., Smarkola, C., Kopacz, S.M., & Ishoof, S.B. (2009). Perinatal outcomes in nutritionally monitored obese pregnant women: a randomized clinical trial. Journal of the National Medical Association, 101(6), 569-577.

Vesco, K.K., Karanja, N., King, J.C., Gillman, M.W., Leo, M.C., Perrin, N., . . . Stevens, V.J. (2014). Efficacy of a group-based dietary intervention for limiting gestational weight gain among obese women: a randomized trial. Obesity, 22(9), 1989-96.

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov