Surveillance of Preconception Health Indicators in Behavioral Risk Factor Surveillance System: Emerging Trends in the 21st Century

Abstract

Objectives:

This article assesses emerging trends in the 21st century, if any, in preconception health indicators among women of reproductive age.

Methods:

This is a secondary analysis of cross-sectional data from the Behavioral Risk Factor Surveillance System (BRFSS), 2003–2010. Subjects were a sample of noninstitutionalized, 18–44-year-old, nonpregnant, women in the United States (n=547,177) grouped into two categories, 2003–2006 (n=275,630) and 2007–2010 (n=271,547). Overall crude and adjusted prevalence odds ratios were calculated for preconception indicators before 2006 and after 2006.

Results:

Significant improvements were found for any and heavy alcohol use, smoking, social and emotional support, moderate/vigorous physical activity, and having had an influenza shot in the last year. In contrast, having a medical condition (i.e., diabetes, high blood pressure, asthma, or obesity), and self-reported health significantly worsened. No change was found for mental distress, HIV testing, and having a routine checkup.

Conclusions:

As the 21st century unfolds, emerging trends suggest that we need to focus on educating women, providers, and public health advocates about improved health before pregnancy, especially for women with chronic conditions.

Introduction

In 2006, the Centers for Disease Control and Prevention (CDC) issued a call to action regarding preconception health.¹ This was not the first national push to integrate preconception health into our systems of care in the United States. One of the first federal articles to discuss the importance of prepregnancy health was published in 1979.² In 1985, the Institute of Medicine (IOM) advocated for preconception risk reduction counseling and health education in the seminal report, “Preventing Low Birthweight.”³ Healthy People 2000 goals set a national target for 60% of primary care physicians to provide preconception care.⁴ Since 2006, national efforts have continued, with the implementation of programs and educational sessions and practice collaboratives across the country.^5
–7 Government funds were used to incorporate preconception care activities in state level initiatives,⁸ and clinical care guidelines were updated to explicitly include preconception healthcare in all health encounters for women of reproductive age.^9,10 This groundswell of activity in the 21st century built on the growing evidence that preconception care can modify behavioral and medical risk factors known to impact pregnancy outcomes.^5,11
–13

Along with the call to action, the CDC identified specific surveillance systems that provide data that should be monitored regularly regarding the preconception health status of women.¹ One recommended data source, the Behavioral Risk Factor Surveillance System (BRFSS), collects information on health risk behaviors and healthcare access. Specific BRFSS indicators recommended to be monitored for preconception include general health (self-rated health status), social determinants of health (women who graduated high school), healthcare (i.e., health insurance coverage, routine doctor visits, routine clinical Pap smear, influenza vaccination), risk factors (i.e., current smoking, alcohol use, fruit and vegetable consumption, overweight and obesity, moderate to vigorous physical activity), mental, emotional, and social health (i.e., general mental distress, emotional and social support), and chronic conditions (i.e., diabetes, hypertension, and asthma status).^14,15 Annual surveillance of these BRFSS indicators is limited to women aged 18–44 years, which means that women who will not become pregnant (i.e., hysterectomy, sterilization, same sex partners, or effective birth control) are included in the surveillance. BRFSS has a family planning module that asks about fertility and pregnancy intention, but those questions are not asked in every year or in every state. In spite of that limitation, core BRFSS indicators are collected across all 50 states, often available within 1 year after the survey, weighted to represent the general population, and are the only source in many states of timely related behavioral risk data.¹⁶

A number of studies have explored BRFSS data among women of reproductive age. For example, one study focused on trends in health risks and behaviors for nonpregnant women, comparing data from1991–1992 with data from 2000–2001. They found significant increases in overweight (23.8%), obesity (17.3%), smoking (25.7%), high blood pressure (10.9%), and diabetes (4.9%) in the 2000–2001 time period.¹⁷ A more recent study extended this research by comparing prevalence data of women of reproductive age in 2001 with women in 2009, reporting significant increases in the prevalence of obesity (39%), asthma (23%), diabetes (45%), hypertension (16%), and high cholesterol (35%) and, in contrast, significant improvements in smoking (21% decrease) and inactivity (7% decrease) for nonpregnant women of reproductive age.¹⁸ Other studies have pooled multiple years of BRFSS data to evaluate more stable prevalence rates of risk factors among women of reproductive age. One such study explored health-related quality of life by pooling data from 1998, 2000, and 2001, reporting frequent mental distress as highly prevalent among women aged 18–44 who have asthma (18.8%).¹⁹ Kieffer et al.²⁰ pooled BRFSS data from 2001–2003 to compare differences between women of reproductive age with and without a history of gestational diabetes (hGDM), finding no significant differences between groups in levels of physical activity, fruit and vegetable consumption, or smoking. An extension of this literature would include preconception indicators recommended by the CDC to be tracked through BRFSS to include not only risk factors and chronic conditions but also general health, healthcare, and mental, emotional, and social health and by comparing differences of pooled datasets that consider relative changes throughout a time period rather than between two more distal time periods.

This article evaluates preconception health indicators as recommended by the CDC, using the BRFSS data for women of reproductive age. We posit that a temporal and systematic assessment of preconception health indicators recommended by the CDC will inform our understanding of trends in behavioral risks among women of reproductive age as well as inform strategies that may be needed for future improvements to the health of women of reproductive age in the United States. This article analyzes trends in the prevalence of health indicators of women of reproductive age for two time periods, 2003–2006 and 2007–2010.

Materials and Methods

Design, setting, and subjects

A cross-sectional, secondary analysis of data from the BRFSS, (2003–2010, was conducted. The BRFSS provides data from core questions (fixed and rotating) that are asked in all states in the United States, allowing for the creation of national estimates on important health indicators. Eligible subjects were a sample of noninstitutionalized, 18–44 year old, nonpregnant women (n=547,177). Women were grouped into one of two 4-year categories, 2003–2006 (n=275,630) and 2007–2010 (n=271,547), based on their year of interview.

Measures

Demographic characteristics

Self-reported race/ethnicity (white, non-Hispanic, and other) and household income (<$35,000 and at least $35,000) were coded dichotomously. Self-reported age (18–20, 21–34, 35–44 years) was tertiary, and education (less than high school, high school graduate or more) was a dichotomous variable. Women had current health insurance if they answered yes to: Do you have any kind of healthcare coverage, including health insurance, prepaid plans, such as HMOs, or government plans, such as Medicare? Women indicating they were married or a member of an unmarried couple were coded as married, and women who were divorced, widowed, separated, or never married were coded as not married. Women who indicated they were employed for wages or self-employed were coded as employed; women who were out of work, homemakers, students, or retired were coded as other.

Health indicators

Behavioral risk factors examined included any alcohol use, heavy drinking, smoking, moderate or vigorous exercise, any medical conditions, nutrition, perceived general health status, mental health, and social and emotional support. Most questions were core survey questions; however, rotating core questions are identified below. Women reporting any alcohol use answered yes to the question: During the past 30 days, have you had at least one drink of any alcoholic beverage, such as beer, wine, a malt beverage or liquor? Similarly, heavy drinking was also a calculated variable available in the datasets, defined as women who, on average, had more than one drink per day in the last 30 days. Current smokers indicated that they smoked on some days or everyday. General health was recoded into a dichotomous variable (i.e., excellent, very good, and good; or fair and poor) based on the following question: Would you say that in general your health is …?” Consuming five or more fruits or vegetables per day was a calculated variable for fruit and vegetable servings per day (rotating core question in odd years). Moderate or vigorous activities in a usual week were indicated if a respondent answered yes to the following (rotating core question in odd years) question: Now, thinking about the moderate physical activities you do [fill in "when you are not working" if employed or self-employed] in a usual week, do you do moderate activities for at least 10 minutes at a time, such as brisk walking, bicycling, vacuuming, gardening, or anything else that causes small increases in breathing or heart rate?

General mental distress was defined as answering ≥14 days to the following question: Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good? Social and emotional support, which was not added until 2005, was calculated as yes if a respondent answered always or usually to the core question: How often do you get the social and emotional support you need? Finally, a composite measure of having a medical condition was created, and a woman was classified as such if a doctor had ever told her that she had diabetes (fixed core question), high blood pressure (rotating core question in odd years), asthma (fixed core question), or a body mass index (BMI) of ≥30 (fixed core question).

Health Screenings

Health screenings and test variables were all dichotomous yes/no variables. Fixed core questions included an HIV test (Have you ever been tested for HIV?), an annual routine examination, a question added in 2005 (How long has it been since you last visited a doctor for a routine checkup?), and a flu shot within the last year (During the past 12 months, have you had a flu shot?)

Analytic plan

Pooled data were weighted and analyzed using the complex survey methodology commands in Stata 11.0 to account for nonresponse and noncoverage biases. The advantage of pooling 4 years of data was that they provided more stable estimates of measures over time and greater statistical power. All analyses were conducted at an alpha of 0.05. Bivariate chi-square analyses examined the association of year grouping with demographic characteristics and health indicators. Simple logistic regression estimated the crude prevalence odds ratios (cPOR) and 95% confidence intervals (CI) of year grouping with health indicators. To account for distributional differences between the groupings by year, predictive marginals were calculated to compute standardized estimates. Adjusted prevalence odds ratios (aPOR) and 95% CIs for the effects of year grouping on each lifestyle and screening factor were calculated using multivariable logistic regression, controlling for race, age, marital status, education, income, employment, and health insurance. For all variables found both to be statistically different and to have a relative effect of >10% between comparative years, a graph of predictive marginal by year was created.

Results

An analysis of demographic characteristics compared 2003–2006 data to 2007–2010 data, finding a significantly higher prevalence of older and married women making at least $35,000 a year, who had health insurance, and who had more than a high school education in the 2007–2010 years. These demographic characteristics were used as covariates in the logistic regression analysis. Table 1 provides a comparison of these indicators between the two time periods.

Table 1.

Demographic Characteristics by Time Period, Nonpregnant Women, 18–44 Years Old, Behavioral Risk Factor Surveillance System, 2003–2010 (n=547,177)

Characteristic	2003–2006 (n=275,630) Prevalence % (95% CI)	2007–2010 (n=271,547) Prevalence % (95% CI)	p value
Race/ethnicity			<0.001
White, non-Hispanic	63.4 (63.0–63.8)	62.1 (61.7–62.4)
Nonwhite	36.6 (36.2–37.0)	37.9 (37.6–38.3)
Age			<0.001
18–20	9.3 (9.1–9.6)	9.3 (9.1–9.5)
21–34	49.6 (49.3–50.0)	48.2 (47.9–48.6)
35–44	41.1 (40.7–41.4)	42.5 (42.2–42.8)
Marital status			<0.001
Not married	39.3 (38.9–39.7)	37.9 (37.6–38.3)
Married	60.7 (60.3–61.1)	62.1 (61.7–62.4)
Education			<0.001
Less than high school	10.8 (10.5–11.1)	10.0 (9.9–10.3)
High school or more	89.2 (88.9–89.5)	90.0 (89.7–90.2)
Income			<0.001
<$35,000	42.5 (42.1–42.9)	37.8 (37.5–38.2)
At least $35,000	57.5 (57.1–57.9)	62.2 (61.8–62.5)
Employment			<0.001
Employed	63.6 (63.3–64.0)	61.2 (60.9–61.6)
Other	36.4 (36.0–36.8)	38.8 (38.4–39.1)
Health insurance			0.12
No	20.0 (19.7–20.4)	19.7 (19.4–20.0)
Yes	80.0 (79.6–80.3)	80.3 (80.0–80.6)

CI, confidence interval.

Prevalence estimates and unadjusted ORs for health indicator and health screening factors are presented in Table 2. Table 2 indicates that compared to 2003–2006, women completing the BRFSS in 2007–2010 were 10% less likely to drink any alcohol (cPOR 0.90, 95% CI 0.89-0.82), 9% less likely to be heavy alcohol drinkers (cPOR 0.91, 95% CI 0.87-0.95), 19% less likely to smoke (cPOR 0.81, 95% CI 0.79-0.83), 6% more likely to eat five or more daily fruit and vegetable servings (cPOR 1.06, 95% CI 1.02-1.09), 6% more likely to report moderate or vigorous activity (cPOR 1.06, 95% CI 1.02-1.09), 6% more likely to report having social and emotional support (cPOR 1.06, 95% CI 1.02-1.09); and 66% more likely to have had an influenza shot (cPOR 1.66, 95% CI 1.62-1.70). However, women in 2007–2010 were also 14% more likely to report a chronic medical condition (cPOR 1.14, 95% CI 1.12-1.17). There were no significant differences found for general health, mental distress, HIV testing, and annual routine checkups.

Table 2.

Weighted Prevalence Estimates and Crude Odds Ratios of Selected Risk Factors, by Time Period Comparing 2007–2010 to 2003–2006, Nonpregnant Women, 18–44 Years, Behavioral Risk Factor Surveillance System, 2003–2010, (n=547, 177)

	2003–2006 (n=275,630) Prevalence % (95% CI)	2007–2010 (n=271,547) Prevalence % (95% CI)	cOR (95% CI)
Behavioral
Any alcohol use	53.4 (53.1–53.8)	50.9 (50.5–51.3)	0.90 (0.89–0.92)
Heavy drinking	5.2 (5.0–5.3)	4.7 (4.5–4.9)	0.91 (0.87–0.95)
Smoking	22.3 (22.1–22.6)	18.8 (18.5–19.1)	0.81 (0.79–0.83)
Excellent, very good, good general health	89.0 (88.8–89.3)	88.8 (88.6–89.0)	0.97 (0.94–1.01)
5 or more daily fruit/vegetable servings	24.6 (24.2–25.0)	25.7 (25.2–26.1)	1.06 (1.02–1.09)
Mental distress	13.3 (13.1–13.6)	13.1 (12.9–13.3)	0.98 (0.95–1.01)
Social/emotional support	79.9 (79.5–80.4)	80.8 (80.5–81.1)	1.06 (1.02–1.09)
Moderate or vigorous activity	50.1 (49.5–50.6)	51.4 (50.9–51.9)	1.06 (1.02–1.09)
Any medical condition	36.3 (35.9–36.7)	39.4 (39.1–39.8)	1.14 (1.12–1.17)
Clinical
HIV test	54.2 (53.8–54.6)	54.5 (54.2–54.9)	1.01 (0.99–1.03)
Annual routine checkup	67.6 (67.1–68.1)	67.2 (66.8–67.5)	0.98 (0.95–1.01)
Influenza shot	18.6 (18.3–18.8)	27.5 (27.2–27.8)	1.66 (1.62–1.70)

cOR, crude odds ratio.

Predictive marginals and adjusted estimates controlling for race, age, marital status, education, income, employment, and health insurance are shown in Table 3. The adjusted ORs were similar to the crude ORs, with significant improvements maintained for any alcohol use, heavy alcohol use, smoking, fruit and vegetable consumption, social and emotional support, physical activity, and influenza shot. Significant decrements remained for any medical condition. The decreased ORs of general health (aPOR 0.94, 95% CI 0.90, 0.97) were negligible, but significant. Annual predicted marginals were calculated for all variables that showed significant and relative change before and after 2006, including any alcohol use, binge alcohol use, smoking, any medical condition, and influenza shot (Table 4). Figure 1 shows the slow but consistent reduction of any alcohol use and smoking over time and concurrent increases in any chronic condition, and getting the influenza shot.

FIG. 1.

Yearly predicted marginals for selected indicators.

Table 3.

Adjusted Odds Ratios and Predictive Marginals for Selected Health Indicators, Comparing 2007–2010 to 2003–2006, Nonpregnant Women, 18–44 Years, Behavioral Risk Factor Surveillance System, 2003–2010 (n=547,177)

	2003–2006 (n=275,630) Predictive marginal (95% CI)	2007–2010 (n=271,547) Predictive marginal (95% CI)	aOR (95% CI)
Behavioral
Any alcohol use	55.0 (54.8–55.4)	52.6 (52.2–53.0)	0.90 (0.88–0.92)
Heavy drinking	5.3 (5.1–5.5)	5.0 (4.8–5.1)	0.94 (0.89–0.98)
Smoking	22.1 (21.8–22.4)	19.4 (19.1–19.7)	0.84 (0.81–0.86)
Excellent, very good, good general health	89.5 (89.2–89.7)	88.8 (88.6–89.1)	0.94 (0.90–0.97)
5 or more daily fruit/vegetable servings	25.0 (24.6–25.5)	25.7 (25.3–26.2)	1.04 (1.01–1.07)
Mental distress	13.1 (12.9–13.4)	13.3 (13.0–13.5)	1.02 (0.98–1.05)
Social/emotional support	80.3 (79.9–80.8)	81.1 (80.8–81.4)	1.05 (1.02–1.09)
Moderate or vigorous activity	50.9 (50.4–51.5)	52.0 (51.5–52.6)	1.05 (1.01–1.08)
Any medical condition	36.9 (36.5–37.2)	40.3 (39.9–40.7)	1.16 (1.13–1.19)
Clinical
HIV test	55.8 (55.4–56.2)	56.1 (55.7–56.5)	1.01 (0.99–1.04)
Annual routine checkup	67.8 (67.3–68.3)	67.1 (66.8–67.5)	0.97 (0.94–1.01)
Influenza shot	18.8 (18.5–19.1)	27.8 (27.5–28.1)	1.68 (1.64–1.73)

Adjusted for race, age, marital status, education, income, employment, health insurance.

aOR, adjusted odds ratio.

Table 4.

Yearly Predicted Marginals (95% Confidence Interval) of Selected Health Indicators, Nonpregnant Women, 18–44 Years, Behavioral Risk Factor Surveillance System, 2003–2010 (n=547,177)

	2003	2004	2005	2006	2007	2008	2009	2010
Behavioral
Any alcohol use	55.9 (55.5–56.4)	55.3 (54.9–55.7)	54.7 (54.4–55.0)	54.1 (53.9–54.4)	53.5 (53.3–53.8)	52.9 (52.6–53.2)	52.3 (51.9–52.7)	51.7 (51.2–52.1)
Smoking	23.3 (22.9–23.7)	22.6 (22.3–22.9)	21.8 (21.6–22.0)	21.1 (20.9–21.3)	20.4 (20.2–20.6)	19.7 (19.4–19.9)	19.0 (18.7–19.3)	18.3 (18.0–18.7)
Any medical condition	36.7 (36.2–37.2)	37.2 (36.8–37.6)	37.8 (37.5–38.1)	38.3 (38.0–38.6)	38.9 (38.6–39.1)	39.4 (39.1–39.7)	39.9 (39.6–40.3)	40.5 (40.0–41.0)
Screening/tests
Influenza shot	16.9 (16.5–17.2)	18.5 (18.2–18.8)	20.2 (20.0–20.5)	22.1 (21.8–22.3)	24.0 (23.8–24.2)	26.1 (25.8–26.4)	28.3 (27.9–28.6)	30.6 (30.1–31.0)

Calculated from multivariable adjusted models, including continuous year index, race, age, marital status, education, income, employment, health insurance.

Discussion

Significant improvements were found for a number of preconception health indicators, including drinking any alcohol (10% reduction), heavy drinking alcohol (6% reduction), smoking (16% reduction), social and emotional support (5% increase), moderate or vigorous physical activity (5% increase), and having an influenza shot within the past year (68% increase). These are important improvements that occurred in the health of women and may result from such things as successful public health promotion campaigns for getting a flu shot. Future strategies might focus on those indicators with negligible increases (i.e., heavy alcohol use,²¹ social and emotional support,²² and physical activity^23,24) in order to strengthen and continue the positive trajectory of the body of indicators surveying the health of women of reproductive age.

Building on the findings of Hayes et al.,¹⁸ significant threats to women's preconception health were also found in having a medical condition (16% increase), and self-reported health (6% decrease). Indeed, the majority of the increases in medical conditions are likely due to increases in obesity. The lack of any significant change in mental distress, HIV testing, and having an annual routine checkup between the two time periods was an important concern. Strategies to reduce general mental distress²⁵ and improve the rate of routine doctor visits²⁶ have been shown to improve the future birth outcomes for women of reproductive age. Strategies to revert the increasing trend of binge drinking and medical conditions,^27,28 however, should be a priority in future efforts designed to improve the health of women of reproductive age.

We have work ahead of us in terms of achieving Healthy People 2020 national objectives for women of reproductive age.²⁹ For example, in terms of education, although a significant increase was found for graduating high school (from 89.2% to 90.0%), the actual increase is negligible, and education remains an important predictor of the health of women and their children.²² No improvements were found in healthcare access, with rates at 80.3% between 2007 and 2010, well below the national target of 100%.³⁰ The negligible or lack of improvement in these social determinants of health may be linked to the economic stressors in the latter U.S. period of this study. Furthermore, although improvements were found in smoking (19.4% in 2007–2010) and the annual influenza shot (27.8% in 2007–2010), these rates are far removed from the Healthy People 2020 objectives of no more than 12% for smoking and at least 60% for having the annual influenza shot. Finally, rates increased in the 2007–2010 time period for chronic conditions (52%), going in the opposite direction, increasing the divide between current health of women of reproductive age and national objectives.

This analysis is subject to several limitations. The BRFSS does not include follow-up birth outcome data, so it is impossible to calculate the risk for poor birth outcomes contingent on the behavioral risk factors and health screenings considered in this evaluation. Additionally, because BRFSS is a cross-sectional survey, we cannot claim that when women change from one year (2003–2006) to another (2007–2010), their risk factors also change. The BRFSS data could not be restricted by fertility status or pregnancy intention; thus, results are for women of reproductive age and may not generalize to preconceptional women or women intending a pregnancy. The BRFSS relies on self-reported information, which may be subject to errors. In addition, BRFSS is conducted only with people living in households with a telephone, and the growing reliance on cell phones in our country may bias the results of this sample of 18–44-year-old women. Strengths of this study include a very large sample that is representative of the U.S. population as whole and nationally recommended indicators that cover social determinants, health behaviors, and clinical encounters. We believe these strengths outweigh the limitations, highlighting important areas to focus efforts for improving the health of women as we move forward as a nation.

Conclusions

For preconception health recommendationsto be fully realized, changes are needed in clinical practice, public health support, and health coverage.³¹ It is especially of concern that there is no change in the ongoing access of routine clinical care for women of reproductive age, as this is a major thrust of the call to action. In fact, according to the Kaiser Foundation,³² many of these risk factors are often worse for low-income and minority women. Furthermore, whereas the trends in smoking, alcohol use, and influenza prevention have improved (although less than optimal), the binge drinking and chronic conditions worsening trends are very important concerns. Although it has been reported that clinicians discuss diet and exercise with 67% of their female patients, the growing concerns about tobacco and alcohol use among women are discussed with much less frequency in clinical encounters.³² Often, it takes time for better practices to diffuse through the practices of clinicians and into the awareness and education of consumers. Such practices include improved risks and behaviors for women, preconception and interconception services for all women, and reduced disparities.¹ As the 21st century continues to unfold, more work is needed in educating women, providers, and public health advocates about improved health before pregnancy, especially for women with chronic conditions.

Footnotes

Disclosure Statement

The authors have no conflicts of interest to report.

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