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) | Categories Physical Health | DOI: 10.26054/0KYE1P6XVT


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.


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.


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.


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.


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.


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


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.


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.


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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.


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Qingqing Hu

Division of Public Health, Department of Family & Preventive Medicine, University of Utah School of Medicine

Jihyun Lee

Division of Public Health, Department of Family & Preventive Medicine, University of Utah School of Medicine

Jeannette Nelson

Division of Public Health, Department of Family & Preventive Medicine, University of Utah School of Medicine

Marci Harris

Division of Public Health, Department of Family & Preventive Medicine, University of Utah School of Medicine

Rebekah H. Ess

Division of Public Health, Department of Family & Preventive Medicine, University of Utah School of Medicine

Charles R. Rogers

Division of Public Health, Department of Family & Preventive Medicine, University of Utah School of Medicine

Jessica Sanders

Division of Family Planning, Department of Obstetrics and Gynecology, University of Utah School of Medicine

James VanDerslice

Division of Public Health, Department of Family & Preventive Medicine, University of Utah School of Medicine

Joseph Stanford, MD, MSPH

Department of Family and Preventive Medicine, Division of Public Health, University of Utah

Karen Schliep, PhD

Department of Family and Preventive Medicine, Division of Public Health, University of Utah