Skip to main content
Download PDF
Download ePub

A cost-effectiveness evaluation of a dietitian-delivered telephone coaching program during pregnancy for preventing gestational diabetes mellitus

Cost Effectiveness and Resource Allocation volume 22, Article number: 18 (2024) Cite this article

Abstract

Background

This study aimed to evaluate the cost-effectiveness of a telehealth coaching intervention to prevent gestational diabetes mellitus (GDM) and to calculate the breakeven point of preventing GDM.

Methods

Data to inform the economic evaluation model was sourced directly from the large quaternary hospital in Brisbane, where the Living Well during Pregnancy (LWdP) program was implemented, and further supplemented with literature-based estimates where data had not been directly collected in the trial. A cost-effectiveness model was developed using a decision tree framework to estimate the potential for cost savings and quality of life improvement. A total of 1,315 pregnant women (49% with a BMI 25-29.9, and 51% with a BMI ≥ 30) were included in the analyses.

Results

The costs of providing routine care and routine care plus LWdP coaching intervention to pregnant women were calculated to be AUD 20,933 and AUD 20,828, respectively. The effectiveness of the LWdP coaching program (0.894 utility) was slightly higher compared to routine care (0.893). Therefore, the value of the incremental cost-effectiveness ratio (ICER) was negative, and it indicates that the LWdP coaching program is a dominant strategy to prevent GDM in pregnant women. We also performed a probabilistic sensitivity analysis using Monte Carlo simulation through 1,000 simulations. The ICE scatter plot showed that the LWdP coaching intervention was dominant over routine care in 93.60% of the trials using a willingness to pay threshold of AUD 50,000.

Conclusion

Findings support consideration by healthcare policy and decision makers of telehealth and broad-reach delivery of structured lifestyle interventions during pregnancy to lower short-term costs associated with GDM to the health system.

Background

Excessive gestational weight gain (GWG) occurs in 40 to 60% of pregnancies [1], especially for the 50% of women who commence their pregnancy classified above a healthy weight [2]. Weight gain above recommendations increases the risk of many adverse health outcomes, particularly gestational diabetes mellitus (GDM), and large-for-gestational-age babies [1]. Increased adverse outcomes during pregnancy contribute significantly to the cost of antenatal health care provision [3]. Long-term, women and their infants are also more likely to develop complications arising from the development of obesity and diabetes [4], with associated healthcare costs [5].

GDM affects around 15% of pregnancies in Australia and is a common and potentially serious pregnancy complication which is more common in those with excess GWG [6]. GDM is persistent, elevated blood glucose levels first detected in pregnancy which can result in birthing and neonatal complications [7]. A meta-analysis has shown that excessive GWG increases the risk of GDM by a factor of 1.4, regardless of pre-pregnancy body mass index [8]. The economic burden of treating GDM greatly increases the costs to the health system of providing pregnancy care [9, 10], with women also bearing financial costs of treatment through blood glucose monitoring equipment, increased healthcare appointments and medication [11].

Recommendations for healthy GWG developed by the Institute of Medicine (IOM) [4] are included in several pregnancy care guidelines internationally [12] including Australia [13]. Interventions that include dietary counselling, physical activity and weight monitoring have successfully reduced GWG in research and clinical settings [14,15,16,17], and benefit individual outcomes [16, 18], including prevention of GDM [17,18,19]. Additionally, lifestyle interventions in pregnancy appear to be cost-effective with good return on investment [20].

However, women who start pregnancy above a healthy weight may experience greater barriers to achieving healthy weight gain and require more intensive support [21]. Furthermore, traditional models of face-to-face antenatal support (beyond routine care), including dietetic appointments, have limited uptake or are poorly attended [22,23,24,25].

The Living Well during Pregnancy (LWdP) program is a dietitian-delivered telephone coaching service for women at risk of excessive GWG [26], which was associated with improvements in dietary and physical activity behaviours [27]. This study performed an economic evaluation of the program as a potential intervention to prevent GDM.

Methods

Aims

The aim of this study was to calculate the cost-effectiveness of the LWdP coaching intervention to prevent GDM. A secondary aim was to calculate the breakeven point of preventing GDM.

Study design and setting

The LWdP was implemented into usual care at a quaternary metropolitan hospital in 2018. A detailed description of program and its planned evaluation (including a cost-effectiveness evaluation) are available in the published protocol [26]. The evaluation outcomes completed as a hybrid effectiveness-implementation study has been reported elsewhere [28].

The Living Well during Pregnancy program (LWdP)

The LWdP program was delivered in addition to routine antenatal care for those women who were referred or self-refer. The program aimed to support pregnant women at high-risk of excess GWG to track within their recommended weight gain range for their pre-pregnancy body mass index (BMI) based on IOM recommendations [4] through changing eating and activity behaviours [26]. This included healthy eating, and physical activity, consistent with dietary and physical activity guidelines for pregnancy [26]. Women were eligible for up to 10 telephone coaching calls over their pregnancy and were provided with a participant workbook. Accredited Practising Dietitians with experience in providing antenatal care services who had undergone additional training in motivational interviewing were trained specifically for the delivery of the program [26]. A key component of the program was continuity of care through allocation of the same Dietitian throughout the duration of the program. The program was adapted for pregnancy from the Healthy Living after Cancer program [29].

Routine antenatal care

In Australia, pregnant women birthing in the public system receive free antenatal care provided by midwives, shared care with a general practitioner, obstetrician or a combination of these [30].

Data

Data to inform the economic evaluation model was sourced directly from the large quaternary hospital in Brisbane, where LWdP was implemented and further supplemented with literature-based estimates where data had not been directly collected in the trial. The hospital manages 4500 births per year. Details of maternal anthropometry (height, pre-pregnancy weight, BMI, weight at 36 weeks), pregnancy complications such as GDM, onset of labour, mode of delivery and foetal outcomes (gender, weight, outcomes, head circumference, APGAR scores, neonatal nursery admissions, special care nursery admissions and congenital abnormalities) were sought from the clinical data systems. Information regarding admission date/time, discharge date/time, AR-DRG, International Classification of Diseases 10 (ICD-10) including diagnosis and procedure codes, Australian Classification of Health Interventions (ACHI) codes and cost were sought from the Health Funding and Analysis Unit of the participant Hospital and Health Service costing systems.

Economic evaluation

A cost-effectiveness analysis using hospital data from 2020 was performed using a decision tree framework to estimate the potential for cost savings and quality of life improvement measured in Quality Adjusted Life Years (QALYs) from the wider implementation of the LWdP coaching program. A decision tree model was built in TreeagePro® to compare the coaching intervention to an alternative strategy of routine care to women with a BMI of 25 kg/m2 or greater, or who gain weight too quickly during early pregnancy in order to prevent GDM.

Women were classified as underweight (BMI < 18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2) or obese (≥ 30 kg/m2) according to the World Health Organization [31]. Women classified as healthy weight and underweight were not included in the model as these women were not the primary focus of the intervention. Termination of pregnancy (spontaneous or planned) was also not included in the model as the majority occur prior to the intervention commencing. Stillbirth was excluded as these were < 1% of all births and did not have a material effect on the results of the model (tested in structural uncertainty prior to final model specification).

Figure 1 presents the model structure. The diagram shows that the routine care strategy considers two alternatives: the probability of having a BMI classified as being overweight or being obese during pregnancy. In the obese arm, pregnant women may develop GDM or not. Women were further stratified into type of birth using Australian Refined Diagnosis Related Groups (AR-DRG) codes. These are vaginal delivery minor complexity, vaginal delivery intermediate/major complexity, caesarean delivery minor complexity, and caesarean delivery intermediate/major complexity. The categorisation is influenced by the diagnoses meeting the ICD-10-AM criteria to code (10th Edition), the Diagnosis Complexity level (DCL) assigned to each code and the resultant Episode Clinical Complexity Score (ECCS). A minor delivery is associated with an ECCS of < 2, whereas an intermediate/major complexity is associated with a ECCS ≥ 2. In the no GDM arm of the obese chance node, the same chain of events has been considered outlined for the GDM arm (represented in clone 1). In the overweight arm of the routine care strategy, the flow of events was the same as described for the obese BMI arm (Fig. 1). In the alternative strategy, coaching, pregnant women follow exactly the same pathways depicted in the routine care strategy (represented in clone 2).

We examined the cost-effectiveness of the telehealth coaching intervention over routine care to prevent GDM by estimating the Incremental cost-effectiveness ratio (ICER). The ICER serves as a valuable tool in informing decision-making when evaluating the funding decision of a new drug or intervention. In Australia, there is no official statement regarding an explicit willingness-to-pay threshold for funding new medicines. However, the Pharmaceutical Benefits Advisory Committee (PBAC) tended to not recommend a drug for listing if the cost per additional year of life gained exceeded AUD 76,000. Conversely, they were unlikely to reject a drug if the cost per additional year of life gained was less than AUD 42,000 [32]. A newly developed medication that costs less than AUD 50,000 per Quality-Adjusted Life Year (QALY) gain is more likely to receive a recommendation for financial support in Australia [33].

We applied different thresholds of AUD 40,000 to 70,000 with an interval of AUD 10,000 assumed for decision-making to explore the influence on the incremental cost-effectiveness of coaching compared with routine care as part of sensitivity analyses.

Fig. 1
figure 1

Simplified diagram of the decision tree model used to analyze the cost-effectiveness of coaching to prevent GDM

Full size image

Costs

The data from 142 women referred to the program was available for costing the intervention. Costs included the staff time for the coaching intervention, costed at local wage rates (AUD 55.8/hour for a Senior Dietitian). The average cost of LWdP coaching intervention was AUD 311.

For costs of births, all babies birthed between 1 September 2016 to 31 August 2017 were included in the cost analysis. Costs associated with antenatal outpatient activity from 1 December 2015 and post-natal outpatient activity to 26 February 2018 were also included in the analysis [31].

Total costs for each type of birth per BMI category were calculated as the sum of mother and infant costs for respective hospital admissions and pre or post birth related outpatient appointments for the mother including appointments for gynaecology urology, continence, obstetrics complex pregnancy and midwifery lactation.

Probabilities

The probability of having a major complexity versus a birth with minor complexity for each type of delivery (vaginal vs. caesarean) by GDM status were calculated for overweight and obese women using the proportion of births in each category from the cost analysis.

The LWdP program evaluation was not powered to detect a difference in the prevention of GDM. Therefore, the effectiveness of the coaching intervention at preventing GDM was determined from a systematic literature review of relevant similar interventions [16].

Utilities

Utility scores were not collected in the LWdP program evaluation. Utility values for complex and uncomplex vaginal delivery and caesarean section were sourced from Kohler et al. (2018) [34] which reported quality of life weights for vaginal birth and caesarean section. Please refer to Supplementary Table 1 for the disutility associated with complex vaginal and caesarean delivery.

Ethical approval for the study was provided by the Royal Brisbane and Women’s Hospital Human Research Ethics Committee (HREC/17/QRBW/159) and was conducted according to the guidelines laid down in the Declaration of Helsinki.

Results

Figure 2 briefly explains the flow of participants into the final analytic sample for costs and probabilities. Initially, the data set contained the information of 5,122 female participants. After exclusions, a total of 3,531 pregnant women were included in the subsample analyses. We restrict our sample to pregnant women who were either overweight or obese. The final analytic sample consisted of 1,315 pregnant women, with 51% classified as overweight and 49% classified as obese.

Fig. 2
figure 2

Analysis sample for costs and probabilities

Full size image

Table 1 reports the relative probabilities of the types of births including major and minor complexity by BMI category with 95% confidence intervals and associated costs. Women with GDM were more likely to experience major complexity during both vaginal and caesarean deliveries. There was also a higher proportion of having minor complexity during caesarean births for women with GDM. Costs were broadly similar between women classified as overweight and obese for type of delivery. For example, the costs of major complexity in vaginal delivery among overweight and obese pregnant women were AUD 30,498, and AUD 32,311, respectively.

Supplementary Table 1 presents the full list of variables used in the model including the cost of coaching, the cost of each category of birth outcomes, and the relative probabilities of different birth outcomes. Additionally, for sensitivity analysis purposes, Supplementary Table 1 presents the lower and upper values for each studied parameter for the one-way sensitivity analysis. A 30% change in the base values of each cost parameter has been considered to determine the lower and upper values, and 95% confidence intervals of the relative probabilities of various events are used. Further, supplementary Table 1 provides the distributions of the parameters used for performing probabilistic sensitivity analysis.

Cost-effectiveness results

The base case cost-effectiveness result is presented in Table 2. The cost of the LWdP coaching intervention was lower than the routine care intervention, and the effectiveness of the LWdP strategy was slightly higher compared to the routine care strategy. As a result, the value of ICER turns to be negative, and the LWdP coaching intervention strategy is dominant.

Multiple one-way sensitivity analyses of the cost-effectiveness of the LWdP coaching intervention against routine care in pregnant women is shown in Fig. 3. The tornado diagram shows a range of ICERs associated with the uncertainty range of most influential parameters. According to the tornado diagram, the value of ICER is sensitive to a number of parameters. The parameter that has the highest impact on ICER is shown on the top bar. For example, the sensitivity of ICER is highest to the costs of major complex birth outcome through vaginal delivery among overweight women with GDM, followed by costs of major complex birth outcome through C-section among overweight women with GDM. Additionally, the tornado diagram shows that the value of ICER is least sensitive to the value of disutility associated with an uncomplex caesarean delivery. The figure clearly shows that the value of ICERs increases if the value of all parameters increases.

Table 1 Costs and relative probabilities of birth outcomes through vaginal and caesarean delivery mode by BMI classification and GDM status
Full size table
Table 2 Costs, effectiveness, and incremental cost-effectiveness estimates
Full size table
Fig. 3
figure 3

Tornado diagram - Incremental Cost Effectiveness Ratio

Full size image

Figure 4 displays the probabilistic sensitivity analysis using Monte Carlo simulation to estimate the cost-effectiveness of the coaching intervention versus routine care through 1,000 simulations. Each simulation generates a single outcome, unique ICER, represented as a green or maroon point on the plot. The ellipse represents the 95% confidence ellipse of outcomes. The ICE scatter plot showed that coaching was more effective and less costly in 93.60% of the trials using a willingness to pay (WTP) threshold of AUD 50,000. Additionally, we demonstrated how different thresholds may influence the incremental cost-effectiveness of coaching compared with routine care as part of sensitivity analyses. The ICE scatter plot (s) showed that coaching was more effective and less costly in over 90% of the trials when a threshold of AUD 40,000 to 70,000 with an interval of AUD 10,000 was assumed for decision-making (Supplementary Information Fig. 1). Therefore, coaching was an undominated cost-effective strategy.

Fig. 4
figure 4

Monte Carlo simulations scatter plot of incremental cost-effectiveness of coaching compared with routine care with a willingness to pay (WTP) of AUD50, 000

Full size image

Discussion

This study aimed to evaluate the cost effectiveness of a dietitian delivered telephone lifestyle coaching program (LWdP) in preventing the risk of GDM in women with a BMI ≥ 25 kg/m2. A woman with GDM had a higher probability of a vaginal or caesarean delivery of intermediate/major complexity at each BMI category, increasing the cost of care. The analysis demonstrated that a coaching intervention that prevents GDM is likely to be cost saving to the health system when considering the immediate pregnancy and delivery costs.

Few studies have examined the cost effectiveness of lifestyle interventions to prevent GDM [35]. The findings of the current analysis are consistent with a recent Australian study examining lifestyle intervention for preventing GDM, hypertensive disorders of pregnancy or both [35]. Using a risk ratio for GDM from a large meta-analysis of intervention studies, Bailey and colleagues (2020) reported that lifestyle intervention to prevent GDM was cost neutral, and therefore likely cost effective, based on the cost associated with immediate pregnancy outcomes [35]. The findings are further supported by several studies examining the cost effectiveness of healthy eating (with or without physical activity) interventions to prevent excess gestational weight gain [36, 37]. While behavioural interventions for pregnant women are associated with higher costs to deliver the additional care [37], structured behavioural interventions emphasising healthy eating with physical activity are preferable and cost-effective for reducing gestational weight gain [20, 36, 38].

In contrast to these findings, a behavioural intervention based on diet and physical activity targeting pregnant women with a BMI of ≥ 30 was deemed not cost-effective when compared to standard care in improving QALYs in UK [39]. However, the authors of this study did not include pregnancy delivery costs, with follow up until 36 weeks gestation included [39].

The findings of the present study need to be considered in the context of several strengths and limitations. A comprehensive decision analytic model (decision tree) was developed to capture the therapeutic and financial implications of the cost-effectiveness of the LWdP coaching intervention in preventing GDM. The study considered utility value as the outcome measure in the model with most of the parameters (e.g., costs of the intervention and probabilities) estimated using real world data from Australian pregnant women to boost validity and reliability of the results.

However, this analysis was carried from the Australian healthcare system perspective and might not be generalizable to other countries with different healthcare systems. Furthermore, the value of the intervention effectiveness, utility, and disutility weights associated with caesarean delivery were extrapolated from the existing literature and may not reflect the cultural, and environmental differences in the Australian context. However, there are some constraints that cannot be avoided when modelling reality with a decision-analytic model. While the cost savings are modest, there could be substantial ongoing benefits to the mother and child from avoidance of birthing complications and improvements in health behaviours that are not able to be captured in the decision tree model as this model only captures costs and effects around the pre-birth, time of birth and limited follow up (6 months post- delivery time horizon) at the birthing institution. It is well established that dietary intake and gestational weight gain during pregnancy impact not only on pregnancy outcomes [1, 40], but on the long term health of mothers and their offspring [41]. Intervening during pregnancy when women are in contact with the health system, and motivated for change is likely to have long term benefits in terms of preventing obesity, diabetes, and cardiovascular disease for both woman and offspring [42]. These future cost savings need to be considered in economic modelling. Future research should explore alternative and hybrid delivery models for intervention during pregnancy, and the long-term health benefits of nutrition intervention from the perspective of the woman, offspring and family should be included in future cost-effectiveness studies.

Women with GDM during pregnancy were much more likely to have births– whether vaginal delivery or caesarean of increased complexity. The cost of delivery with increased complexity was much higher than without. Therefore, preventing GDM is likely to reduce complications during birth, subsequently giving both mother and baby a better experience and start to life as well as reducing costs to the health system.

Conclusion

This study adds to the growing body of evidence that evidence-based lifestyle interventions that focus on supporting nutrition behaviour change are cost-effective in preventing GDM from the Australian healthcare system perspective. Taking a life course approach to preventive health care, focussing on supporting women developing healthy eating behaviours prior to conception and in early pregnancy is likely to have a long-term positive health and economic impact on women and that of her offspring. Healthcare policy and decision makers need to consider telehealth and broad-reach delivery of structured lifestyle interventions during pregnancy to lower short term costs associated with GDM to the health system. Additionally, long-term cost savings are likely but require further investigation.

Data availability

The datasets analysed for the current study are available from the corresponding author on reasonable request and in accordance to local restrictions governing the privacy of information obtained from medical records.

References

  1. Goldstein RF, Abell SK, Ranasinha S, Misso ML, Boyle JA, Harrison CL, et al. Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women. BMC Med. 2018;16(1):1–14.

    Article  Google Scholar 

  2. de Jersey SJ, Nicholson JM, Callaway LK, Daniels LA. A prospective study of pregnancy weight gain in a ustralian women. Aust N Z J Obstet Gynaecol. 2012;52(6):545–51.

    Article  PubMed  Google Scholar 

  3. Moran PS, Wuytack F, Turner M, Normand C, Brown S, Begley C, et al. Economic burden of maternal morbidity–A systematic review of cost-of-illness studies. PLoS ONE. 2020;15(1):e0227377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Institute of Medicine, National Research Council. Weight gain during pregnancy: reexamining the guidelines. National Academies Press; 2009.

  5. Kuhle S, Muir A, Woolcott CG, Brown MM, McDonald SD, Abdolell M, et al. Maternal pre-pregnancy obesity and health care utilization and costs in the offspring. Int J Obes. 2019;43(4):735–43.

    Article  Google Scholar 

  6. Australian Institute of Health and Welfare. Incidence of gestational diabetes in Australia. 2019 [cited 2022 February]; Available from: https://www.aihw.gov.au/reports/diabetes/incidence-of-gestational-diabetes-in-australia/contents/what-is-gestational-diabetes.

  7. Catalano PM, McIntyre HD, Cruickshank JK, McCance DR, Dyer AR, Metzger BE, et al. The hyperglycemia and adverse pregnancy outcome study: associations of GDM and obesity with pregnancy outcomes. Diabetes Care. 2012;35(4):780–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Brunner S, Stecher L, Ziebarth S, Nehring I, Rifas-Shiman SL, Sommer C et al. Excessive gestational weight gain prior to glucose screening and the risk of gestational diabetes: a meta-analysis. Diabetologia. 2015 2015/10/01;58(10):2229–37.

  9. Dall TM, Yang W, Halder P, Pang B, Massoudi M, Wintfeld N, et al. The economic burden of elevated blood glucose levels in 2012: diagnosed and undiagnosed diabetes, gestational diabetes Mellitus, and Prediabetes. Diabetes Care. 2014;37(12):3172–9.

    Article  PubMed  Google Scholar 

  10. Cade TJ, Polyakov A, Brennecke SP. Implications of the introduction of new criteria for the diagnosis of gestational diabetes: a health outcome and cost of care analysis. BMJ Open. 2019;9(1):e023293.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Craig L, Sims R, Glasziou P, Thomas R. Women’s experiences of a diagnosis of gestational diabetes mellitus: a systematic review. BMC Pregnancy Childbirth. 2020;20(1):1–15.

    Article  Google Scholar 

  12. Harrison CL, Teede H, Khan N, Lim S, Chauhan A, Drakeley S, et al. Weight management across preconception, pregnancy, and postpartum: a systematic review and quality appraisal of international clinical practice guidelines. Obes Rev. 2021;22(10):e13310.

    Article  PubMed  Google Scholar 

  13. Health Q. Queensland Clinical Guideline for the Management of Obesity in Pregnancy. In: Health Q, editor.2015.

  14. Shieh C, Cullen DL, Pike C, Pressler SJ. Intervention strategies for preventing excessive gestational weight gain: systematic review and meta-analysis. Obes Rev. 2018;19(8):1093–109.

    Article  CAS  PubMed  Google Scholar 

  15. Hector D, Hebden L. Prevention of excessive gestational weight gain: An evidence review to inform policy and practice. 2013.

  16. Teede HJ, Bailey C, Moran LJ, Bahri Khomami M, Enticott J, Ranasinha S, et al. Association of Antenatal Diet and Physical activity–based interventions with gestational weight gain and pregnancy outcomes: a systematic review and Meta-analysis. JAMA Intern Med. 2022;182(2):106–14.

    Article  PubMed  Google Scholar 

  17. International Weight Management in Pregnancy, Collaborative G. Effect of diet and physical activity based interventions in pregnancy on gestational weight gain and pregnancy outcomes: meta-analysis of individual participant data from randomised trials. BMJ. 2017;358:j3119–j.

    Article  Google Scholar 

  18. Cantor AG, Jungbauer RM, McDonagh M, Blazina I, Marshall NE, Weeks C, et al. Counseling and behavioral interventions for healthy weight and weight gain in pregnancy: evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2021;325(20):2094–109.

    Article  PubMed  Google Scholar 

  19. Schoenaker DA, De Jersey S, Willcox J, Francois ME, Wilkinson S. Prevention of gestational diabetes: the role of dietary intake, physical activity, and weight before, during, and between pregnancies. Seminars in reproductive medicine. Thieme Medical Publishers, Inc.; 2020. pp. 352–65.

  20. Bailey C, Skouteris H, Harrison CL, Hill B, Thangaratinam S, Teede H, et al. A comparison of the cost-effectiveness of lifestyle interventions in pregnancy. Value Health. 2022;25(2):194–202.

    Article  PubMed  Google Scholar 

  21. de Jersey SJ, Mallan K, Callaway L, Daniels LA, Nicholson JM. A Cross Sectional Comparison of Predisposing, reinforcing and Enabling Factors for Lifestyle Health Behaviours and Weight Gain in healthy and overweight pregnant women. Matern Child Health J. 2017;21(3):626–35.

    Article  PubMed  Google Scholar 

  22. Olander EK, Atkinson L. Obese women’s reasons for not attending a weight management service during pregnancy. Acta Obstet Gynecol Scand. 2013;92(10):1227–30.

    Article  PubMed  Google Scholar 

  23. Robertson N, Ladlow B. Effect of individual dietetic intervention on gestational weight gain and associated complications in obese pregnant women. Aust N Z J Obstet Gynaecol. 2018;58(3):274–7.

    Article  PubMed  Google Scholar 

  24. Opie RS, Neff M, Tierney AC. A behavioural nutrition intervention for obese pregnant women: effects on diet quality, weight gain and the incidence of gestational diabetes. Aust N Z J Obstet Gynaecol. 2016;56(4):364–73.

    Article  PubMed  Google Scholar 

  25. Lee A, Karpavicius J, Gasparini E, Forster D. Implementing a diet and exercise program for limiting maternal weight gain in obese pregnant women: a pilot study. Aust N Z J Obstet Gynaecol. 2012;52(5):427–32.

    Article  PubMed  Google Scholar 

  26. de Jersey S, Meloncelli N, Guthrie T, Powlesland H, Callaway L, Chang AT, et al. Implementation of the Living Well during Pregnancy Telecoaching Program for women at high risk of excessive Gestational Weight Gain: protocol for an effectiveness-implementation hybrid study. JMIR Res Protoc. 2021;10(3):e27196.

    Article  PubMed  PubMed Central  Google Scholar 

  27. de Jersey S, Meloncelli N, Guthrie T, Powlesland H, Callaway L, Chang AT et al. Outcomes from a hybrid implementation-effectiveness study of the living well during pregnancy Tele-coaching program for women at high risk of excessive gestational weight gain. BMC Health Serv Res. 2022 2022/05/03;22(1):589.

  28. !!!. INVALID CITATION!!! [27, 28].

  29. Eakin EG, Hayes SC, Haas MR, Reeves MM, Vardy JL, Boyle F, et al. Healthy living after Cancer: a dissemination and implementation study evaluating a telephone-delivered healthy lifestyle program for cancer survivors. BMC Cancer. 2015;15(1):992.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Department of Health and Aged Care. Providing pregnancy care. 2020 [cited 2023 29 November]; Available from: https://www.health.gov.au/resources/pregnancy-care-guidelines/part-b-core-practices-in-pregnancy-care/providing-pregnancy-care-services.

  31. Organization WH. Obesity: preventing and managing the global epidemic. 2000.

  32. George B, Harris A, Mitchell A. Cost-effectiveness analysis and the consistency of decision making: evidence from pharmaceutical reimbursement in Australia (1991 to 1996). PharmacoEconomics. 2001;19(11):1103–9.

    Article  CAS  PubMed  Google Scholar 

  33. Taylor C, Jan S. Economic evaluation of medicines. Aust Prescr. 2017;40(2):76–8.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kohler S, Sidney Annerstedt K, Diwan V, Lindholm L, Randive B, Vora K, et al. Postpartum quality of life in Indian women after vaginal birth and cesarean section: a pilot study using the EQ-5D-5L descriptive system. BMC Pregnancy Childbirth. 2018;18(1):1–13.

    Article  Google Scholar 

  35. Bailey C, Skouteris H, Harrison CL, Boyle J, Bartlett R, Hill B, et al. Cost effectiveness of antenatal lifestyle interventions for preventing gestational diabetes and hypertensive disease in pregnancy. PharmacoEconomics-open. 2020;4(3):499–510.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Broekhuizen K, Simmons D, Devlieger R, Van Assche A, Jans G, Galjaard S, et al. Cost-effectiveness of healthy eating and/or physical activity promotion in pregnant women at increased risk of gestational diabetes mellitus: economic evaluation alongside the DALI study, a European multicenter randomized controlled trial. Int J Behav Nutr Phys Activity. 2018;15(1):1–12.

    Article  Google Scholar 

  37. Gyllensten H, Haby K, Berg M, Premberg Å. Cost effectiveness of a controlled lifestyle intervention for pregnant women with obesity. BMC Pregnancy Childbirth. 2021;21(1):1–12.

    Article  Google Scholar 

  38. O’Sullivan EJ, Rokicki S, Kennelly M, Ainscough K, McAuliffe FM. Cost-effectiveness of a mobile health-supported lifestyle intervention for pregnant women with an elevated body mass index. Int J Obes. 2020;44(5):999–1010.

    Article  Google Scholar 

  39. Poston L, Bell R, Briley AL, Godfrey KM, Nelson SM, Oteng-Ntim E et al. Improving pregnancy outcome in obese women: the UK pregnancies Better Eating and Activity randomised controlled Trial. 2017.

  40. Kibret KT, Chojenta C, Gresham E, Tegegne TK, Loxton D. Maternal dietary patterns and risk of adverse pregnancy (hypertensive disorders of pregnancy and gestational diabetes mellitus) and birth (preterm birth and low birth weight) outcomes: a systematic review and meta-analysis. Public Health Nutr. 2019;22(3):506–20.

    Article  Google Scholar 

  41. Gilmore LA, Klempel-Donchenko M, Redman LM. Pregnancy as a window to future health: excessive gestational weight gain and obesity. Seminars in perinatology. Elsevier; 2015. pp. 296–303.

  42. Langley-Evans S. Nutrition in early life and the programming of adult disease: a review. J Hum Nutr Dietetics. 2015;28:1–14.

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank all women who participated in the evaluation of the Living Well during Pregnancy Program, clinical and administrative staff from Maternity Services at the Royal Brisbane and Women’s Hospital and Health Costing and Analysis staff. The authors also wish to acknowledge co-investigators for their contribution on the clinical evaluation of the Living Well during Pregnancy study Leonie Callaway, Shelley Wilkinson, and Hilary Powlesland.

Funding

SdeJ is supported by a Metro North Health Clinician Research Fellowship. The Royal Brisbane and Women’s Hospital Foundation Project Grant supported program evaluation.

Author information

Authors and Affiliations

  1. Department of Dietetics and Food Services, Royal Brisbane and Women’s Hospital, Metro North Health, Brisbane, Australia

    Susan de Jersey & Taylor Guthrie

  2. Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia

    Susan de Jersey, Syed Afroz Keramat, Nina Meloncelli, Taylor Guthrie & Tracy Comans

  3. Centre for Allied Health Research, Royal Brisbane and Women’s Hospital, Metro North Health, Brisbane, Australia

    Angela Chang

  4. School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia

    Elizabeth Eakin

Authors
  1. Susan de Jersey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Syed Afroz Keramat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Angela Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nina Meloncelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Taylor Guthrie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elizabeth Eakin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tracy Comans
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SdJ, EE and TC were involved in the conception and design of the study. AC, TG and NM contributed to data collection and reviewed the manuscript. AC, SAK and TC performed the analysis. SdJ, TC, AC, SAK contributed to the interpretation of the works. SdJ, SAK and NM drafted the manuscript. All authors contributed to reviewing and revising the manuscript and approved the final manuscript.

Corresponding author

Correspondence to Susan de Jersey.

Ethics declarations

Ethics approval and consent to participate

This study and all procedures involving research study participants was given ethical approval from the Royal Brisbane and Women’s Hospital Human Research Ethics Committee (HREC/17/QRBW/159) and was conducted according to the guidelines laid down in the Declaration of Helsinki. All methods were carried out in accordance with relevant guidelines and regulations. Written informed consent was obtained from all participants in the LWdP program. Retrospective data collected for the historical control group was granted a waiver of consent under Sect. 282 of the Public Health Act 2005 (RD007988).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Table 1

: Summary of input parameters for the cost-effectiveness model

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Jersey, S., Keramat, S.A., Chang, A. et al. A cost-effectiveness evaluation of a dietitian-delivered telephone coaching program during pregnancy for preventing gestational diabetes mellitus. Cost Eff Resour Alloc 22, 18 (2024). https://doi.org/10.1186/s12962-024-00520-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12962-024-00520-9

Keywords

Cost Effectiveness and Resource Allocation

ISSN: 1478-7547

Contact us