The Baby-Friendly Hospital Initiative (BFHI): An Early Cross-Sectional Analysis of PRAMS Phase 8 Data on Hospital Practices and Breastfeeding Outcomes in Utah and Wyoming

Abstract

Introduction: Breastfeeding has immediate and long-term benefits for both maternal and child health.  This study examines the association between Baby-Friendly Hospital Initiative (BFHI) experiences and breastfeeding outcomes in the Mountain West region.

Methods: A cross-sectional (retrospective secondary data analysis) was performed using the 2016 Pregnancy Risk Assessment Monitoring System (PRAMS) data. The participants were derived from a stratified random sample of 2,013 women living in Utah and Wyoming who recently had a live birth and who were surveyed on BFHI practices. The association between BFHI experiences and breastfeeding duration were assessed using crude and adjusted Poisson regression models, controlling for other BHFI experiences and maternal age, pre-pregnancy BMI, household income, smoking, alcohol, delivery method, and number of days spent in the hospital post delivery

Results: 82.4% and 82.3% of women from Utah and Wyoming, respectively, reported breastfeeding for 2 months or longer. After controlling for other BFHI experiences and potential confounders, the one shared BFHI experience that was associated with breastfeeding for 2 months or longer vs less than 2 months was starting breastfeeding in the hospital (adjusted prevalence ratio [aPR]=1.49, 95% CI (1.12, 1.98) in Utah and aPR=2.03, 95% CI (1.13, 3.64) in Wyoming. Among women in Utah and Wyoming, only 5 of 7 BFHI steps were significant for breastfeeding duration in at least one state.

Conclusion: There is substantial epidemiological support for health benefits to both mother and infant for exclusive breastfeeding to 6 months and prolonged breastfeeding until at least 1-year. Our findings suggest that women who initiate breastfeeding in the hospital may be more likely to breastfeed for a longer duration.

Introduction

Breastmilk is the most nutritious food for infant development and studies show that breastfeeding promotes optimal health outcomes for the mother/infant dyad that have lifelong implications. Additionally, the American Academy of Pediatrics promotes sustained breastfeeding practices for at least the first year of life.1–3 Although the U.S. Centers for Disease Control and Prevention (CDC) has reported steady increases in breastfeeding practices in the US, attributed in part to support of the medical community and the Baby-Friendly Hospital Initiative (BFHI),4–7 the prevalence of exclusive breastfeeding is below the Healthy People 2020 targets.2

The Baby-Friendly Hospital Initiative (BFHI) is a joint effort started by UNICEF and the WHO to protect, promote, and support breastfeeding practices.8,9 More specifically, the BFHI seeks to increase positive in-hospital experiences such as initiating breastfeeding within 1 hour of birth, feeding on demand, and breastfeeding education and support, while limiting experiences that hinder early initiation and duration of breastfeeding, such as giving pacifiers or giving gift baskets that include formula.10 Although studies have demonstrated BFHI’s success in improving breastfeeding beyond six weeks, only 28% of U.S. annual births take place in Baby-Friendly certified hospitals. Additionally, implementation strategies vary across hospitals, and there is still widespread use of supplementation and pacifiers.11–14

Furthermore, BFHI implementation in rural hospitals is limited; thus studies that explore BFHI experiences and breastfeeding outcomes in rural regions are needed.4,15 Close to 80% of the population in both Utah and Wyoming live in rural areas,16 and both states have birth rates above the national average, with Utah recording the highest birthrate in 2017.17 In addition, out of the 591 Baby-Friendly facilities in the nation, there is only 1 located in each of these states.18 No previous studies that have included an analysis of BFHI experiences between Utah and Wyoming because prior to 2016, the Wyoming PRAMS did not include any questions about BFHI experiences.

Therefore, the purpose of this study was to describe the current state of Baby-Friendly practices in Utah and Wyoming and to assess whether BFHI experiences impact breastfeeding termination and duration among new mothers. The study was approved by the University Of Utah Institutional Review Board (IRB) and determined exempt.

Materials and Methods

Study Design & Population

This study analyzed cross-sectional population-level data for mothers who had recently given birth to a live infant in Utah or Wyoming in 2016 and who completed the CDC Pregnancy Risk Assessment Monitoring System (PRAMS) Phase 8 questionnaire. The CDC developed PRAMS in 1987 as an ongoing, nationwide surveillance system that is state-specific in its sampling scheme and operated within local health departments to collect data related to behaviors and experiences of mothers pre-pregnancy, during the prenatal period, and in the immediate post-natal period.19,20,21 The PRAMS initiative aims to promote safe motherhood, as well as reduce low birth weight and infant mortality.22 The PRAMS questionnaire collects information on an array of topics such as maternal knowledge, attitudes and behaviors about pregnancy, breastfeeding, infant health, physical abuse, stress and social support, maternal use of alcohol and tobacco products, and contraception, among others.23 The recruitment process involves the random selection of potential participants from a sample of birth certificates indicating a recent live birth between two and six months post-partum.24

Utah oversamples mothers with low education and infants with low birthweight while Wyoming oversamples by maternal race and infant birthweight to ensure that the data is representative of the smaller, higher risk populations.25,26 Using birth certificates, new mothers are randomly selected, within stratified sampling scheme, to participate in completing the PRAMS survey.20 Utah and Wyoming select approximately 200 and 140 women each month, respectively, who delivered live births and are at two to six months post-partum.20,27 Selected mothers receive an introductory letter by mail, followed by a survey that is mailed a week after the introductory letter is sent, followed by third and fourth survey attempts mailed to non-respondents.20 Next, an interviewer contacts those non-respondents who received the mailed survey.20  The surveys and phone interviews are available and may be administered in English and Spanish to accommodate Spanish-speaking mothers when necessary.28 Mothers who recorded “Hispanic” on birth certificate information received surveys in English and Spanish.29 The expected response rate in Utah and Wyoming is 60% – 65% following the CDC protocol.20,28 The actual response rate for UT in 2016 was 65% and 63% for WY. Once the surveys are received by the local health department, responses are grouped to document the self-reported prevalence data.28

Data Sources/Measurement: Breastfeeding Initiation/Duration Measures

For this analysis, breastfeeding (BF) termination and duration measures were informed via the Utah Phase 8 (2016) and Wyoming Phase 8 (2016) surveys, which included the following questions: “Did you ever breastfeed or pump breast milk to feed your new baby, even for a short period of time?” Respondents with a “yes” answer were then asked, “Are you currently breastfeeding or feeding pumped milk to your new baby?” If response was “no”, the respondents were asked, “How many weeks or months did you breastfeed or pump milk to feed your baby?”

Data Sources/Measurement: In-Hospital Newborn Care Enhancement Measures

Mothers who reported “yes” to breastfeeding their newborn or giving them pumped breast milk, regardless of the duration, were asked to respond to the following questions with a “yes” or “no” answer about their BFHI experiences: 1) “Hospital staff gave me information about breastfeeding”; 2)“My baby stayed in the same room with me at the hospital”; 3) “I breastfed my baby in the hospital”; 4) “Hospital staff helped me learn how to breastfeed”; 5) “I breastfed in the first hour after my baby was born?”; 6) “My baby was placed in skin-to-skin contact within the first hour of life?”7) “My baby was fed only breastmilk at the hospital”; 8) Hospital staff told me to breastfeed whenever my baby wanted”; 9) “The hospital gave me a breast pump to use”; 10) “The hospital gave me a gift pack with formula”; 11) “The hospital gave me a telephone number to call for help with breastfeeding”; and 12) “Hospital staff gave my baby a pacifier”. The prevalence of BFHI experiences for the study population are found in Table 2 and in Figure 2.

Covariates

In this analysis, the Phase 8 Utah-PRAMS (2016) and Phase 8 Wyoming-PRAMS (2016) were examined to understand if there is an association between in-hospital newborn care enhancement measures as well as early initiation and continuation of BF after delivery.  Key demographic, behavioral and experiential factors were identified as potential confounders through a thorough literature review.6,30–33 Final decisions on potential confounding factors to include were informed by confirming that the factor is associated with one of the BFHI experiences and with the outcome variables of interest (BF termination or duration), that the factor is unequally distributed within the study population, and that the factor is not an intermediary step in the causal pathway from BFHI experiences and the outcome variable.34,35 The covariates selected for this analysis included maternal age (<20, 20-24, 25-34, 35+), maternal body mass index (BMI) (WHO categories: underweight, normal weight, overweight, obese), household (HH) income (≤$28,000, $28,001-$57,000, $57,001-$85,000, over $85,000), smoked in previous 2 years (no/yes), drank alcohol in previous 2 years (no/yes), delivery method (vaginal or C-section), and the number of days the baby stayed in hospital post-delivery (<1 day, 1–2 days, 3–5 days, 6–14 days, >14 days, or still in). Table 1 delineates these population characteristics by BF duration status for the Utah & Wyoming PRAMS 2016 analysis.

Study Size, Methods, and Statistical Analysis

The total number of participants between both states included 2,013 women who completed the state-specific PRAMS survey. An overview of the sample selection process is illustrated in Figure 1. In Utah, the total number of recorded live births that occurred in 2016 was 50,486 and of those 1,400 women completed the Utah-PRAMS.36 The Utah analysis excluded 43 women (3%) who did not respond to the ever breastfed question, and 28 women (2%) whose delivery did not occur in the hospital within the state of Utah in 2016. In Wyoming, the total number of recorded live births that occurred in 2016 is 7,384 and of those, 613 women completed the Wyoming PRAMS survey.37 The Wyoming analysis excluded 28 women (4.6%) who did not respond to the ever breastfed question, and 12 women (2%) whose delivery did not occur in the hospital within the state of Wyoming in 2016. The weighted response rate was 63% in Wyoming.26 After exclusions, a total of 1,901 women (n=1328 from Utah and n=573 from Wyoming) were included in the analysis.

Descriptive population characteristics were used to compare mothers from Utah and Wyoming according to BF initiation, termination or duration. Prevalence ratios (PR) were calculated with 95% confidence intervals (CI) to evaluate the relationship between BF termination or duration and mother’s BFHI experiences, using unadjusted and adjusted Poisson regression. Analyses were completed using SAS version 9.4 (SAS University ed.) and STATA 15.1 (Stata Corp, LLC). Survey data were weighted according to PRAMS methodology such that the sample was representative of all mothers who delivered during 2016 in Utah and Wyoming.

Results

Participants from Utah were 15–45 years old, while participants from Wyoming were 15–43. The average age of participants from both states was 28 years old. Participants from both states completed the survey on an average at 16 weeks postpartum. The interquartile range for Utah was 13–20 weeks with a range of 10–32 weeks, while in Wyoming the interquartile range was 13–18 weeks with a range of 10–37 weeks. The percentage of survey respondents who reported BF initiation was 93.4% in Utah and 90.5% in Wyoming. 69.5% of mothers in Utah and 68% in Wyoming reported they were still BF at the time of survey completion. Exclusive BF was reported by 59.9% in Utah and 70.4% in Wyoming. The average number of weeks mothers breastfed was 12.8 weeks in Utah versus. 12.3 weeks in Wyoming.

         Figure 1 outlines the prevalence of each of the BFHI in each state, with “Baby breastfed in the hospital” having the highest prevalence in each state (95.0% in Utah and 95.1% in Wyoming) and staff giving breast pump for breastfeeding having the lowest prevalence (35.5% in Utah and 24.0% in Wyoming).

Figure 1: Flow diagram explaining the final cohort of women in the analysis

17.7% of participants from Wyoming reported BF <2 months and 82.3% breastfed for 2 months or longer compared vs. 17.6% <2 months and 82.4% in Utah. In both states, women who breastfed for 2 months or longer versus <2 months tended to be older, of normal weight, higher income, non-alcohol consumers, non-smokers, having a vaginal delivery, and fewer days in the hospital (Table 1).

Table 1: Population characteristics by breastfeeding duration

Unadjusted analysis of each BFHI experience and the association on breastfeeding duration for each state was assessed. In Utah, findings indicated that 8 of 12 the experiences—feeding baby in hospital, giving gift with formula, giving breast pump, engaging in skin to skin in the first hour, BF in the first hour, feeding only breastmilk, baby staying in the room, and giving pacifier by staff — were associated either positively or negatively with BF duration (PR=1.59, 95% CI (1.23, 2.04), PR=0.88, 95% (CI 0.83, 0.93), PR=0.89, 95% CI (0.83, 0.96), PR=1.15, 95% CI (1.04, 1.27), PR=1.2, 95% (CI 1.09, 1.33), PR=1.20, 95% CI (1.12, 1.29), PR=1.15, 95% CI (1.03, 1.29), and PR=0.87, 95% (CI 0.82, 0.92) respectively) (Table 2). In Wyoming, findings indicated that 4 of 12 the experiences—feeding baby in hospital, staff giving breastfeeding help telephone number, feeding only breastmilk in the hospital, and giving pacifier by staff — were associated either positively or negatively with BF duration (PR=1.69, 95% CI (1.08, 2.65), PR=1.23 95% (CI 1.06, 1.43), PR=1.22, 95% CI (1.07, 1.39), and PR=0.88, 95% (CI 0.80, 0.97 respectively).

Table 2: Breastfeeding duration
Figure 2: Prevalence of Baby-Friendly Hospital Initiative Steps: Utah and Wyoming PRAMS

After adjusting for other BFHI experiences and confounding factors (maternal age, maternal BMI, HH income, alcohol use, smokers, delivery method and hospital length of stay), the only BFHI experience significant for BF duration (≥ 2 months versus less) for both states was starting breastfeeding in the hospital: adjusted prevalence ration [aPR] = 1.49, 95% CI (1.12, 1.98) in Utah and aPR=2.03, 95% CI (1.13, 3.64) in Wyoming.  In Wyoming only, staff giving BF help telephone number or exclusive feeding of breastmilk in the hospital were significant predictors of longer BF duration, aPR=1.18, 95% CI (1.01, 1.39) and aPR=1.16, 95% CI (1.00, 1.34), respectively, while staff giving breastfeeding information or having baby stay in the hospital room with mother were associated with shorter BF duration, aPR=0.78 (95% CI: 0.64, 0.96) and aPR=0.76 (95% CI: 0.65, 0.90), respectively. Conversely, in Utah, staff who gave a gift that included formula were more likely to report early BF termination (<2 months), aPR=0.93, 95% CI (0.87,0.99)], this was not the finding for Wyoming.

Discussion

This population-based study provides a representative, preliminary description of the current state of breastfeeding for mothers who delivered a live birth in Utah and Wyoming during the first year (2016) Phase 8 PRAMS.  It offers a first ever look at how BFHI experiences impact breastfeeding termination and duration in these rural mountain west states. We found approximately equal prevalence of breastfeeding duration in both states, with approximately 82% of postpartum women reporting breastfeeding for 2 months or more. Additionally, we found that, in relation to BF duration, only women who started breastfeeding in the hospital, had increased likelihood of BF ≥2 months, with Utah having a 49% increase and Wyoming having a 103% increase after adjusting for other BHFI experiences and confounding factors. Wyoming, however, also showed that staff giving breastfeeding help telephone number, and those who fed only breastmilk in the hospital also significantly increased the likelihood of breastfeeding 2 or more months (18% and 16%), respectively. Interestingly those with increased risk of early breastfeeding termination (< 2 months) were those given breastfeeding information by staff and those who had their babies room-in with them 24/7 (22% and 24%), respectively. Those to whom staff gave a pacifier, did not show significant association with breastfeeding duration in either state; however, these were significant risks for early termination for the first multivariate analysis model that controlled only for other BFHI experiences and not the other confounders included in the fully adjusted model. In Utah, those who were given formula were 7% more likely to terminate breastfeeding before 2 months.

Our results regarding starting breastfeeding in the hospital, giving only breast milk, and providing help telephone numbers are consistent with other research.11 Our results showed that giving pacifiers did not significantly impact breastfeeding duration. Although this finding may be counter-intuitive, it is supported by large RCTs that also showed no impact.39 Our findings that showed negative impacts from rooming-in and provision of staff help are contradictory to other findings,12 and may be due to reverse causation (i.e., women who require infant to be in the room with them or who need help from staff breastfeeding may be women who are having greater difficulties breastfeeding and thus it is not the BHFI but rather the difficulty breastfeeding that drives the association).

The limitations present in our study should be considered when interpreting our findings. Reporting biases are likely because data were not available for race/ethnicity or pre-term delivery, both of which are known to impact breastfeeding initiation and duration.31 Similarly, there was no information on the hospitals where the infants were born, and subsequently, no information on the status of the hospital’s Baby-Friendly designation. Possible recall bias may exist in that women who breastfed longer may differ significantly in their recollection of BFHI experiences than those who did not. Additionally, the impact of parity as a potential confounder was not addressed in our analysis. Further, generalizability is limited as the study focuses exclusively on the Mountain West region. However, for states with limited access to BFHI designated hospitals, our findings may be more relevant.

Despite these limitations, there are several strengths of the study.  First, we utilized weighted data to represent all mothers who gave birth from 2016 in Utah and Wyoming. The sample size of the study was also reflective of these populations with weights to ensure the inclusivity of at-risk women. Furthermore, this is the first time Wyoming has included the BFHI experiences question in their PRAMS survey. Thus this study has the unique strength of being the first to compare these two very similar populations.

Conclusion

In conclusion, our results demonstrate the importance of initiating and exclusively breastfeeding in the hospital as well as providing help telephone numbers for women about breastfeeding prior to discharge. More specifically, our findings indicate that small rural hospitals may be able to improve breastfeeding duration by implementing these specific BFHI recommendations. Additionally, our results suggest that giving a gift pack with formula in the hospital is associated with stopping breastfeeding before two months for both states, but providing a pacifier is not associated with breastfeeding duration. There is strong epidemiological support for the health benefits to both mother and infant for exclusive breastfeeding to 6 months and prolonged breastfeeding until at least 1-year, and along with the U.S. Preventive Services Task Force (USPSTF), the WHO and UNICEF we also strongly promote and encourage this practice.1–3,38

Acknowledgements

Data were provided by the Wyoming and Utah Pregnancy Risk Assessment Monitoring Systems (PRAMS) directed by the Utah and Wyoming Departments of Health in coordination with the US Health and Human Services Department and Centers for Disease Control and Prevention.  This study is not representative of the official views of the entities listed.

Sources of Funding:

Dr. Rogers received supportive funds from the National Cancer Institute of the National Institutes of Health (NIH) [grant number K01CA234319]. This report does not represent the official views of the CDC, Utah Department of Health, or the NIH.

References

1. Victora CG, Bahl R, Barros AJDD, et al. Breastfeeding in the 21st century: Epidemiology, mechanisms, and lifelong effect. Lancet. 2016;387(10017):475-490. doi:10.1016/S0140-6736(15)01024-7

2. Dieterich CM, Felice JP, O’Sullivan E, Rasmussen KM. Breastfeeding and Health Outcomes for the Mother-Infant Dyad. Pediatr Clin North Am. 2013;60(1):31-48. doi:10.1016/j.pcl.2012.09.010

3. Eideleman AI. Breastfeeding and the Use of Human Milk. Pediatrics. 2012;129(3):e827-e841. doi:10.1542/peds.2011-3552

4. Munn AC, Newman SD, Mueller M, Phillips SM, Taylor SN. The Impact in the United States of the Baby-Friendly Hospital Initiative on Early Infant Health and Breastfeeding Outcomes. Breastfeed Med. 2016;11(5):222-230. doi:10.1089/bfm.2015.0135

5. Myers D, Turner-Maffei C. Improved Breastfeeding Success Through the Baby-Friendly Hospital Initiative. Am Fam Physician. 2008;78(2):180-182.

6. U.S. Centers for Disease Control and Prevention (CDC). Breastfeeding Report Card, United States 2018.; 2018.

7. Sinha B, Chowdhury R, Sankar MJ, et al. Interventions to improve breastfeeding outcomes: A systematic review and meta-analysis. Acta Paediatr Int J Paediatr. 2015;104:114-135. doi:10.1111/apa.13127

8. UNICEF. Baby-Friendly Hospital Initiative Ten Steps to Successful Breastfeeding.

9. World Health Organization (WHO). Implementation guidance: protecting, promoting and supporting breastfeeding in facilities providing maternity and newborn services – the revised Baby-friendly Hospital Initiative. 2018:56.

10. Baby-Friendly USA Inc. Baby-Friendly USA ~ 10 Steps & International Code. https://www.babyfriendlyusa.org/for-facilities/practice-guidelines/10-steps-and-international-code/. Accessed October 19, 2019.

11. Digirolamo AM, Grummer-Strawn LM, Fein SB, University E. Effect of Maternity-Care Practices on Breastfeeding. Pediatrics. 2008;122(Supplement 2):S43-S49. doi:10.1542/peds.2008-1315e

12. Nickel NC, Labbok MH, Hudgens MG, Daniels JL. The extent that noncompliance with the ten steps to successful breastfeeding influences breastfeeding duration. J Hum Lact. 2013;29(1):59-70. doi:10.1177/0890334412464695

13. Baby-Friendly USA Inc. Baby-Friendly USA ~ Upholding the Highest Standards of Infant Feeding Care. https://www.babyfriendlyusa.org/. Accessed October 24, 2019.

14. Declercq E, Labbok MH, Sakala C, O’Hara MA. Hospital practices and women’s likelihood of fulfilling their intention to exclusively breastfeed. Am J Public Health. 2009;99(5):929-935. doi:10.2105/AJPH.2008.135236

15. Lillehoj CJ, Dobson BL. Implementation of the Baby-Friendly Hospital Initiative Steps in Iowa Hospitals. JOGNN – J Obstet Gynecol Neonatal Nurs. 2012;41(6):717-727. doi:10.1111/j.1552-6909.2012.01411.x

16.  United States Census Bureau. Rural America: Geographical Areas and Rural Data. https://gis-portal.data.census.gov/arcgis/apps/MapSeries/index.html?appid=7a41374f6b03456e9d138cb014711e01. Accessed June 16, 2019.

17.  Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final for 2017. Natl Vital Stat Reports. 2018;67(8):1-49.

18. Baby-Friendly USA Inc. Baby-Friendly Facilities A-Z and by State. https://www.babyfriendlyusa.org/for-parents/baby-friendly-facilities-by-state/. Accessed June 14, 2019.

19. Shulman HB, D’Angelo D V, Harrison L, et al. The Pregnancy Risk Assessment Monitoring System (PRAMS): Overview of design and methodology. Am J Public Health. 2018;108(10):1305-1313. doi:10.2105/AJPH.2018.304563

20. Utah Department of Health. Maternal and Infant Health Program. https://mihp.utah.gov/pregnancy-and-risk-assessment. Accessed June 17, 2019.

21. U.S. Centers for Disease Control and Prevention (CDC). PRAMS Methodology. PRAMS. https://www.cdc.gov/prams/methodology.htm. Accessed June 17, 2019.

22. U.S. Centers for Disease Control and Prevention (CDC). PRAMS. https://www.cdc.gov/prams/index.htm. Published 2018.

23. U.S. Centers for Disease Control and Prevention (CDC). PRAMS Questionnaires. https://www.cdc.gov/prams/questionnaire.htm. Published 2018. Accessed January 8, 2019.

24. Shulman HB, D’Angelo D V., Harrison L, Smith RA, Warner L. The Pregnancy Risk Assessment Monitoring System (PRAMS): Overview of Design and Methodology. Am J Public Health. 2018;108(10):1305-1313. doi:10.2105/AJPH.2018.304563

25. UDOH Maternal & Infant Health Program. Utah PRAMS. Maternal & Infant Health Program.

26. Wyoming Department of Health. Wyoming Pregnancy Risk Assessment Monitoring System (PRAMS) 2017 Surveillance Report.; 2018.

27. Wyoming Department of Health. Hospital-Based Breastfeeding Practices.; 2018.

28. Wyoming Department of Health. Pregnancy Risk Assessment Monitoring System (PRAMS). https://health.wyo.gov/publichealth/chronic-disease-and-maternal-child-health-epidemiology-unit/mch-epi/pregnancy-risk-assessment-monitoring-system-prams/. Accessed May 30, 2019.

29.  Schliep KC, Denhalter D, Gren LH, Panushka KA, Singh TP, Varner MW. Factors in the Hospital Experience Associated with Postpartum Breastfeeding Success. Breastfeed Med. 2019;14(5):334-341. doi:10.1089/bfm.2018.0039

30. Kair LR, Colaizy TT. Association Between In-Hospital Pacifier Use and Breastfeeding Continuation and Exclusivity: Neonatal Intensive Care Unit Admission as a Possible Effect Modifier. Breastfeed Med. 2017;12(1):12-19. doi:10.1089/bfm.2016.0137

31. Pounds L, Shostrom V. Analyzing Factors That Impact Breastfeeding Duration in the Postpartum Period: A Secondary Analysis of PRAMS Data. Breastfeed Med. 2018;13(5):335-340. doi:10.1089/bfm.2018.0020

32. Sipsma HL, Jones K, Nickel NC. Hospital practices to promote breastfeeding: The effect of maternal age. Birth. 2017;44(3):272-280. doi:10.1111/birt.12284

33. Wallwiener S, Müller M, Doster A, et al. Predictors of impaired breastfeeding initiation and maintenance in a diverse sample: what is important? Arch Gynecol Obstet. 2016. doi:10.1007/s00404-015-3994-5

34. LaMorte WW, Sullivan L. Confounding and Effect Measure Modification. http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704-EP713_Confounding-EM/BS704-EP713_Confounding-EM_print.html. Accessed September 24, 2019.

35. Aragon TJ. Population Heath Thinking with Bayesian Networks.; 2019. doi:10.1111/ina.12046

36. Office of Vital Records and Statitics. Births and Deaths : Year Ended … Salt Lake City; 2017.

37. Beaudoin G, Storey M. Vital Statistics Services 2016 Annual Report W. S. § 35-1-404 ( a )( V ). Vol 404.; 2017.

38. Hawkins SS, Stern AD, Baum CF, Gillman MW. Evaluating the impact of the Baby-Friendly Hospital Initiative on breast-feeding rates: A multi-state analysis. Public Health Nutr. 2015;18(2):189-197. doi:10.1017/S1368980014000238

39. O’Connor NR, Tanabe KO, Siadaty MS, Hauck FR. Pacifiers and breastfeeding: A systematic review. Arch Pediatr Adolesc Med. 2009;163(4):378-382. doi:10.1111/j.1523-536x.2009.00336_1.x

Citation

Bliss J, Mensah NA, Rogers CR, Stanford JB, VanDerslice J, Schliep KC. (2020). The Baby-Friendly Hospital Initiative (BFHI): An early cross-sectional analysis of PRAMS Phase 8 data on hospital practices and breastfeeding outcomes in Utah and Wyoming. Utah Women’s Health Review. doi: 10.26054/0KMTC25CW0

PDF

View / download

The Association Between Preconception Body Mass Index and Subfertility Among Hispanic and non-Hispanic Women: A Cross-Sectional Study from Utah’s Pregnancy Risk Assessment Monitoring System Survey (2012–2015)

Abstract

Objective: To investigate the association between pre-pregnancy body mass index (BMI) and subfertility within a population-based cohort, exploring Hispanic ethnicity as a potential effect modifier.

Methods: We used cross-sectional study data from the Utah Pregnancy Risk Assessment Monitoring System from 2012–2015. Relationships between maternal pre-pregnancy BMI and subfertility were evaluated via Poisson regression models with robust error variance, accounting for the stratified survey sampling. Preconception BMI was analyzed continuously and categorically. Women’s subfertility was defined via self-report in two ways: 1) time trying to achieve pregnancy; and 2) report of using fertility-related drugs/medical procedures.

Results: The median age was 27.0; 18.8% were obese, and 15.9% were Hispanic. Women with preconception obesity (BMI>30kg/m2), compared to normal weight women (18.4kg/ m2<BMI<25kg/m2) had a 1.85 (95% CI 1.43, 2.38) higher adjusted prevalence ratio (aPR) for having subfertility defined by time trying and a 1.73 (95% CI 1.20, 2.32) higher aPR for receiving fertility-enhancing drugs/medical procedures. Continuous models indicated a linear relationship between BMI and subfertility (aPR 1.04, 95% CI 1.03, 1.06 for time trying; and 1.06, 95% CI 1.03, 1.10 for receiving fertility-enhancing drugs/medical procedures).

Conclusions: Obese women, but not underweight or overweight women, reported higher subfertility than normal-weight women. Findings among this cohort of at-risk new mothers, oversampled on low education and birth weight and comprised of higher than the national average of Hispanics, indicated a dose-response relationship between obesity and subfertility.

Implications: Our findings highlight the importance of population-oriented obesity prevention for at-risk women with intentions to conceive.

Introduction

Body mass index (BMI) in the U.S. has continued to rise over the last two decades, women of reproductive age included. The 2015–2016 National Health and Nutrition Examination Survey estimated that among women ages 20 to 39 years, 36.5% were obese (BMI >30 kg/m2).1 The association between women’s obesity and subfertility has been established,2 however, existing studies focus on the link between maternal pre-pregnancy BMI and pregnancy outcomes among non-Hispanic white women undergoing fertility treatment.2 Prior studies have been conducted among Asians and African Americans,3,4 but few among Hispanic women.5,6 As the proportion of women with obesity in the U.S. continues to rise, evaluating how BMI and obesity directly impact women of various race/ethnicities is critical to address health-related disparities among under-represented women.

The Hispanic population is of interest for several reasons. Although overall fertility rates in the U.S. decreased from 2007–2017, Hispanics had the largest decline compared to non-Hispanic whites and African Americans.7 Furthermore, maternal pre-pregnancy BMI distribution by race indicates that Hispanics have the largest percent of overweight mothers (29.7%) compared to non-Hispanic white (24.1%), African-American (26.9%), American Indian/Alaskan Native (27.2%), or Asian (19.9%) mothers.1 Lastly, although Hispanic women make up 12.5% of the U.S., Hispanics only use 5.4% of the nation’s infertility care/resources—potentially resulting from the disparities in access to care.8 A better understanding of the factors that contribute to this health disparity are needed, warranting research that includes women of Hispanic ethnicity and explores unique attributes that may influence access to infertility care.

Taking into account potential effect modification by Hispanic ethnicity, our study aimed to investigate the association between pre-pregnancy BMI and women’s subfertility within a population-based cohort of Utah women, comprised of roughly 16% Hispanics.

Methods

Study Design

This is a cross-sectional study using data from the Utah Pregnancy Risk Assessment Monitoring System (PRAMS) survey, which has the standardized data collection methodology developed by the Centers for Disease Control and Prevention (CDC).

Data Sources

Started by the CDC in 1987, the current study’s population stems from the PRAMS nationwide surveillance project, which has the two-fold purpose of decreasing the morbidity and mortality of mothers and infants, and improving their health by reducing adverse outcomes.9 PRAMS is a population-based and state-specific study of women who delivered a live birth, accompanied by their maternal attitudes, behaviors, and experiences before, during, and shortly after pregnancy. The health topics covered in PRAMS are related to the following: prenatal care, attitudes and feelings about previous pregnancy, health insurance coverage, cigarette smoking, drinking, physical abuse, maternal stress, economic status, and infant health status. One key aspect of PRAMS is the stratified systematic sampling, which oversamples on features related to high risk women (e.g., mothers of low-birth-weight infants, those living in high-risk geographic areas, and racial/ethnic minority groups). The design and methodology of PRAMS have been published elsewhere.10

For the current study, the authors used data from the Utah PRAMS Phase 7 (2012–2015) questionnaire (n = 5,770 reflecting an estimated population of 199,905 women [number of women in the population that each respondent represents]). PRAMS Phase 7 Utah sampling was stratified by maternal education and infant birthweight. The design and sampling frame of PRAMS assure a study sample that is representative of Utah’s population–including a 16% prevalence of Hispanic ethnicity.10

Approximately 200 new mothers are randomly selected from Utah birth certificate data each month to participate in PRAMS. New mothers are contacted via mailed questionnaire (available in English and Spanish) multiple times and telephone follow-up. Response rates in Utah were roughly 72% in 2012, 66% in 2013, 69% in 2014, and 67% in 2015, higher than the 60% response rate that the CDC expects.9 Participating womens’ responses are linked to extracted birth certificate data items for analysis. The availability of birth certificate information for all births is the basis for drawing stratified samples and, ultimately, for generalizing results to the state’s entire population of births.9 The PRAMs weighting process produces an analysis weight taking into account the stratified sampling along with nonresponse and noncoverage components.9 The analysis weight of the PRAMs data can be interpreted as the number of women like herself in the population that each respondent represents.9

The study was evaluated by the University of Utah Institutional Review Board (IRB) and determined exempt. 

Outcome Measures

The primary outcome of interest was women’s subfertility which was assessed in two ways. First, subfertility was defined based on self-reported time trying to achieve pregnancy: >12 months for women ≤35 years of age, and >6 months for women > 35 years.11 In the Utah PRAMS Phase 7 (2012–2015) questionnaire, time trying was assessed by two questions: “When you got pregnant with your new baby, were you trying to get pregnant?” and if women answered “yes” they were then asked “How many months were you trying to get pregnant?” with potential responses of 0–3 months, 4–6 months, 7–12 months, 13–24 months, or >24 months. Second, we defined subfertility based on self-reported fertility treatment. If women answered yes they were trying to get pregnant with their new baby, then they were also asked “Did you take any fertility drugs or receive any medical procedures from a doctor, nurse, or other health care worker to help you get pregnant with your new baby?” with potential responses of fertility-enhancing drugs prescribed by a doctor, artificial insemination, assisted reproductive technology, other medical treatment, or “I wasn’t using fertility treatments during the month that I got pregnant with my new baby.”

Exposure Measures

Pre-pregnancy BMI was calculated using birth certificate reported height and weight, and categorized in standard groups for underweight (<18.5 kg/m2); normal (18.5-24.9 kg/m2); overweight (25.0-29.9 kg/m2); and obese (30 kg/m2 or higher).5 Height and weight data were also available via self-report from the PRAMS questionnaire. Given the high correlation of BMI data from both sources (Pearson correlation coefficient=93.5%), missing birth certificate BMI values (n=71) were replaced with PRAMS BMI data.

Covariates

Confounding factors thought to impact both women’s preconception BMI and subfertility were determined based on prior literature. Demographic and health factors included race/ethnicity, maternal education, marital status, family income, health insurance, prior pregnancy/live birth, and preconception maternal age, smoking, drinking, diabetes, hypertension, and depression were all considered potential confounding factors.2-6

Statistical Analysis

Participant characteristics were reported across BMI categories while taking into account the complex survey design of PRAMS.9 The continuous variables (e.g. maternal age, BMI) were reported by median and interquartile range (IQR), and the categorical variables were reported by frequencies and percentages. To evaluate associations between preconception BMI and women’s subfertility, modified Poisson regression models were employed with robust error variance, accounting for the stratified sampling, to estimate prevalence ratios (PR) and 95% confidence intervals (CI), with normal weight as the referent group.12 Additionally, adjusted multinomial logistic regression was used to analyze the association between BMI and multiple categories of months trying to get pregnant (0–3 months, 4–6 months, 7–12 months, 13–24 months, or >24 months). Effect modification by Hispanic ethnicity was evaluated via stratified analyses and tested via the interaction term approach. SAS 9.4 and Stata 15 were used for the analysis.

Results

After excluding the 20 missing values for BMI and 107 missing values for whether women were trying to get pregnant with their new baby, 5,644 women (98.2%) were included in the analyses, reflecting an estimated population size of 196,323 women (Figure 1).

Figure 1. Study Participant Flowchart: Utah PRAMS, 2012–2015

Characteristics of the Mothers

Median BMI of the study population was 23.8 (interquartile range [IQR] 21.1, 28.3) (kg/m2), with BMI categories of underweight (4.7%), normal weight (54.3%), overweight (22.3%), and obese (18.8%) (Table 1). Median age was 27.0 (IQR 24.0, 31.0) years old. The majority of women were parous (67.1%), had health insurance (82.1%), were married (83.2%) and had ≥ 12 years of education (90.3%). Most of the women (84.1%) were White non-Hispanic, while 9.2% were Non-white Hispanic and 6.7% were White Hispanic. Prior to pregnancy, nearly a third of women (30.8%) reported consuming alcohol and 11.2% smoked tobacco. Preconception prevalence of depression, diabetes, and hypertension were 10.1%, 1.3%, and 2.2%, respectively.

Table 1: Demographic, lifestyle and clinical characteristics of women by BMI, Utah PRAMS, 2012–2015, n=5644, reflecting an estimated population size of 196,323 women.

Compared to women of normal weight, obese women were more likely to be older, parous, of Hispanic ethnicity, have no health insurance, of lower education and family income, and report smoking or drinking alcohol in the two years prior to pregnancy. Moreover, obese women were more likely to have been previously diagnosed with diabetes, hypertension, or depression.

Association Between BMI and Subfertility Measures

Among women who were trying to get pregnant with their most recent baby, each unit increase in BMI was associated with a higher adjusted prevalence ratio (aPR) of months trying to get pregnant: 1.03 (95% CI 1.01, 1.05) for 4–6 months, 1.03 (95% CI 1.01, 1.05) for 7–12 months, 1.06 (95% CI 1.04, 1.09) for 13–24 months, and 1.08 (95% CI 1.06, 1.10) for >24 months compared to women who attempted to achieve pregnancy for 0–3 months (Table 2). Obese women were 1.58 times (95% CI 1.22, 2.06), 1.39 times (95% CI 1.03, 1.89), 1.92 times (95% CI 1.36, 2.70), and 3.31 times (95% CI 2.43, 4.50) as likely to have tried 4–6, 7–12, 13–24, and >24 months to get pregnant, respectively, compared to women who had tried 0–3 months.

Table 2: Unadjusted association between BMI and self-reported months trying to get pregnant.

After adjusting for maternal age, income, education, marital status, and race/ethnicity, women with preconception obesity, compared to normal weight women, had a 1.85 (95% CI 1.43, 2.38) higher aPR for having subfertility defined by time trying (Table 3). Continuous models indicated a linear relationship between BMI and subfertility (aPR: 1.04, 95% CI 1.03, 1.06); however, no association was found between underweight (aPR: 0.50, 95% CI 0.22, 1.14) or overweight (aPR: 1.06, 95% CI 0.80, 1.41) status and subfertility compared to normal weight. Similar findings were found for receiving any fertility-related drugs, insemination or in vitro fertilization [IVF]) with obese women having a 73% higher prevalence (95% CI 1.29, 2.32) of these procedures compared to normal weight women. Further adjustment for parity and preconception smoking, alcohol consumption, and depression in all models did not appreciably alter the findings (Table 3), nor did further adjustment for a prior diabetes or hypertension diagnosis. As exemplified in the stratified analyses (Table 4), no effect modification by Hispanic ethnicity was identified by the interaction test (Wald test F-value; P=0.73)

Table 3: Relationship between BMI and months trying to get pregnant or fertility treatment.
Table 4: Relationship between BMI and months trying to get pregnant, stratified by Hispanic ethnicity.

Discussion

Our research among a population-based cohort of women found that obese women, compared to normal weight women, have a 73% and 85% higher probability of experiencing a longer time to pregnancy or using fertility treatment after controlling for a number of sociodemographic and lifestyle factors. No association was found between underweight or overweight women and subfertility; nor was effect modification by Hispanic ethnicity found.

Strengths of the Study

A population-based sample was used and weighted to represent all mothers who gave birth in Utah from 2012–2015. Further, our sample size included a representative proportion of Hispanic ethnicity, and ensured that at-risk women were included. Additionally, the PRAMS questionnaire included detailed information about socio-demographics, reproductive and health history, and lifestyle characteristics, therefore we were able to assess multiple confounding factors that may affect adiposity and subfertility. Finally, subfertility was measured through different ways (time trying and fertility-related drugs/medical procedures).

Limitations of the Data

First, the PRAMS questionnaire collects self-reported data from women who just delivered live births. The reliability of self-reported preconception height, weight, and months trying to get pregnant is dependent on women’s ability to accurately recall, which has been shown to be prone to error.13,14  Second, BMI may not be the best measure to assess women’s obesity because it does not account for ethnicity, age, body composition and shape, or healthy body mass such as muscle.3 The measurements of waist circumstance or waist-hip ratio for central adiposity would be helpful.15 Third, we could not account for BMI of male partners, which might influence the results.16 Fourth, selection bias cannot be ruled out. PRAMS follows a strict protocol for sampling mothers, but Utah’s average response rate for 2012–2015 was 69%. Fifth, certain reproductive disorders such as polycystic ovary syndrome (PCOS) may confound the relationship between BMI and subfertility, but such information was not available in the UT-PRAMS Phase 7 questionnaire. PCOS information has been added to the UT-PRAMS Phase 8 questionnaire (2016 to present) and thus, further research taking into account PCOS diagnosis and/or symptomology is warranted. Finally, perhaps most importantly, this study included only women who had a live birth; the results may differ if women who want to conceive but have not done so successfully yet were included.17

Interpretation

The finding of a relationship between obesity and subfertility agrees with an extensive body of previous literature.2,3,18-34 For instance, Brewer and Balen concluded that obesity impaired both natural and assisted conception, especially in women with a BMI >35 kg/m2.19 Gaskins et al. found that being overweight or obese in female adulthood was associated with modest reductions in fecundity that led to an increase in duration of pregnancy attempt.2 However, the results from our study differ from other studies in that we did not find that preconception overweight (not obese) women and subfertility are associated.2,15,21,24 Conflicting findings may be attributable in part to the fact that prior studies mostly examined women being treated for subfertility.2,21,24 Future studies among non-clinical populations are needed to clarify the relationship between adiposity and subfertility among women not seeking treatment.

Additionally, our findings are consistent with other research conducted in Utah,16 which may be reflective of the relatively good health of the Utah population compared to other states.25 We found no differences in the association of BMI with subfertility among Hispanic women compared to non-Hispanic women. This was most likely due to the relatively small sample size of Hispanic women in our dataset, thus we may have a limited power to detect the disparities between Hispanic and NHW women. However, similarly, Wise and colleagues did not find an association between overweight (BMI of 25.0-29.9 kg/m2) and reduced fecundity among African American women, but did find an association between class 2 and 3 obesity (BMI of ≥ 35.0kg/m2) and reduced fecundity.4 Whether there are clear differences in the effects of adiposity on subfertility among different race and ethnicities has yet to be elucidated. Further population-based research that includes adequate representation of women from various races and ethnicities is warranted before conclusions can be made.

Fertility treatment utilization within our sample was similar to that found in other representative samples. Among our sample of women who reported having sought out fertility treatment, 62.1% reported taking fertility drugs to help them get pregnant while 13.4% reported receiving artificial insemination. A National Survey of Family Growth (NSFG) study reported that nearly half of the women who were trying to get pregnant received drugs to improve ovulation, followed by 13.1% for artificial insemination.26 Because of the small sample sizes for Hispanic women receiving different types of fertility treatment, we were limited in our ability to report the disparities between Hispanic and non-Hispanic women in use of the various fertility treatments. While access to infertility treatment is beyond the scope of this study, given prior research showing that socioeconomic status is significantly associated with the ability to seek out fertility treatment in the US,27 increased equity in access to fertility diagnostics and treatment is needed.28

Health Implications

In brief, this population-based PRAMS study inclusive of at-risk mothers found that preconception obesity, but not overweight or underweight, was associated with women’s subfertility, consistent with prior research. There was no difference by Hispanic ethnicity nor when evaluating subfertility in multiple ways. Given inconsistent findings to date, we are wary to make recommendations for clinicians or policy makers based on our findings. Further population-based research adequately including women and couples of various races and ethnicities is needed to help better understand whether healthy women who are overweight, but not obese, are comparable to normal weight women in regards to ability to achieve a pregnancy. This research is important given that women deserve to have preconception counsel in regards to risk factors for subfertility based not on intuition but rather findings from sound and methodologically rigorous research.

Acknowledgements

Author Contributions:

Concept and design: Dr. Karen C. Schliepand Qingqing Hu

Data analysis: Qingqing Hu, Jihyun Lee, Jeannette Nelson, Marci Harris, Rebekah H. Ess

Drafting of Manuscript: Qingqing Hu, Jihyun Lee

Critical revision of the manuscript: Drs.Charles R. Rogers, Jessica Sanders, James VanDerslice, and Joseph B. Stanford

Final approval: Dr. Karen C. Schliep

Sources of Funding:

This work was supported by Dr. Rogers’s funds from the National Cancer Institute of the National Institutes of Health (NIH) [grant number K01CA234319].

Disclosure of Potential Conflicts of Interest:

None reported

Additional Contributions:

Data were provided by the Utah Pregnancy Risk Assessment Monitoring System (PRAMS), a project of the Utah Department of Health (UDOH), the Office of Vital Records and Health Statistics of the UDOH, and the Center for Disease Control and Prevention (CDC) of the U.S. Health and Human Services Department. This report does not represent the official views of the CDC, Utah Department of Health, or the NIH.

References

1.  Branum A, Kirmeyer S, Gregory E. Prepregnancy body mass index by maternal characteristics and state: data from the birth certificate, 2014. National Vital Statistics Reports: From the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System. 2016;65:1–11.

2.  Gaskins AJ, Rich-Edwards JW, Missmer SA, Rosner B, Chavarro JE. Association of fecundity with changes in adult female weight. Obstetrics and Gynecology 2015;126:850–858.

3. Loy SL, Cheung YB, Soh SE, Ng S, Tint MT, Aris IM, et al. Female adiposity and time-to-pregnancy: a multiethnic prospective cohort. Human Reproduction 2018;33:2141–2149.

4. Wise LA, Palmer JR, Rosenberg L. Body size and time-to-pregnancy in black women. Human Reproduction 2013;28:2856–2864.

5. Luke B. Adverse effects of female obesity and interaction with race on reproductive potential. Fertility and Sterility 2017;107(4):868-877.

6. Quinn M, Fujimoto V. Racial and ethnic disparities in assisted reproductive technology access and outcomes. Fertility and Sterility. 2016;105:1119-1123.

7.  Ely DM, Hamilton BE. Trends in Fertility and Mother’s Age at First Birth Among Rural and Metropolitan Counties: United States, 2007-2017. National Center for Health Statistics Data Brief 2018 Oct;323:1–8.

8.  Feinberg EC, Larsen FW, Wah RM, Alvero RJ, Armstrong AY. Economics may not explain Hispanic underutilization of assisted reproductive technology services. Fertility and Sterility. 2007;88:1439–1441.

9. Pregnancy Risk Assessment Measurement Scale (PRAMS). https://www.cdc.gov/prams/index.htm. Accessed January 26, 2020.

10. Shulman HB, D’Angelo DV, Harrison L, Smith RA, Warner L. The Pregnancy Risk Assessment Monitoring System (PRAMS): Overview of Design and Methodology. American Journal of Public Health. 2018;108:1305–1313.

11.  Practice Committee of American Society for Reproductive Medicine. Definitions of infertility and recurrent pregnancy loss: a committee opinion. Fertility and Sterility 2013;99:63.

12.  Petersen MR, Deddens JA. A comparison of two methods for estimating prevalence ratios. BMC Medical Research Methodology. 2008;8:9.

13.  Han E, Abrahms B, Sridhar S, Xu F, Hedderson M. Validity of self-reported pre-pregnancy weight and body mass index classificaiton in an integrated health care delivery system. Paediatric and Perinatal Epidemiology 2016;30:314–9.

14.  Cooney MA, Buck Louis GM, Sundaram R, McGuiness BM, Lynch CD. Validity of Self-Reported Time to Pregnancy. Epidemiology 2009;20:56–59.

15. Wise LA, Rothman KJ, Mikkelsen EM, Sørensen HT, Riis A, Hatch EE. An internet-based prospective study of body size and time-to-pregnancy. Human Reproduction 2009;25:253–264.

16.  Schliep KC, Mumford SL, Ahrens KA, Hotaling JM, Carrell DT, Link M, et al. Effect of male and female body mass index on pregnancy and live birth success after in vitro fertilization. Fertility and Sterility 2015;103:388–395.

17.  Basso O, Juul S, Olsen J. Time to pregnancy as a correlate of fecundity: differential persistence in trying to become pregnant as a source of bias. International Journal of Epidemiology 2000;29:856–861.

18. Klenov VE, Jungheim ES. Obesity and reproductive function: a review of the evidence. Current Opinion in Obstetrics and Gynecology. 2014;26:455–460.

19.  Brewer CJ, Balen AH. The adverse effects of obesity on conception and implantation. Reproduction. 2010;140:347–364.

20.  Gesink Law D, Maclehose RF, Longnecker MP. Obesity and time to pregnancy. Human Reproduction. 2006;22:414–420.

21.  Koning A, Kuchenbecker W, Groen H, Hoek A, Land JA, Khan KS, et al. Economic consequences of overweight and obesity in infertility: a framework for evaluating the costs and outcomes of fertility care. Human Reproduction Update. 2010;16:246–254.

22.  Lash MM, Armstrong A. Impact of obesity on women’s health. Fertility and Sterility. 2009;91:1712–1716.

23.  Poston L, Caleyachetty R, Cnattingius S, Corvalan c, Uauy R, Herring S, et al. Preconceptional and maternal obesity: epidemiology and health consequences. The Lancet Diabetes & Endocrinology. 2016;4:1025–1036.

24. Kort JD, Winget C, Kim SH, Lathi RB. A retrospective cohort study to evaluate the impact of meaningful weight loss on fertility outcomes in an overweight population with infertility. Fertility and Sterility 2014;101:1400–1403.   

25. United Health Foundation America’s Health Rankings 2018 Annual Report. https://www.americashealthrankings.org/learn/reports/2018-annual-report. Accessed January 26, 2020.

26.  Vahratian A. Utilization of fertility-related services in the United States. Fertility and Sterility 2008;90:1317–1319.

27. Farley Ordovensky Staniec J, Webb NJ. Utilization of infertility services: how much does money matter? Health Services Research. 2007;42:971–989.

28.  Boulet SL, Kawwass J, Session D, Jamieson DJ, Kissin DM, Grosse SD. US State-Level Infertility Insurance Mandates and Health Plan Expenditures on Infertility Treatments. Maternal and Child Health Journal. 2019;23:623–632.

29.  Meldrum DR, Morris MA, Gambone JC. Obesity pandemic: causes, consequences, and solutions—but do we have the will? Fertility and Sterility. 2017;107:833–839.

Citation

Hu Q, Lee J, Nelson J, Harris M, Ess R, Rogers CR, Sanders J, VanDerslice J, Stanford JB, & Schliep KC. (2020). The association between preconception body mass index and subfertility among Hispanic and non-Hispanic women: A cross-sectional study from Utah’s Pregnancy Risk Assessment Monitoring System survey (2012–2015). Utah Women’s Health Reviewdoi: 10.26054/0KYE1P6XVT.

PDF

View / download