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volume 37 | number 4 July/August 2012

ABSTRACT

The incidence of preterm birth in the United States varies by race/ethnicity and

socioeconomic status. Given the unsatisfactory reduction in preterm birth with

interventions directed at single risk factors, we examined the preconceptional health of

childbearing-aged women of different racial/ethnic groups to understand the risk prior

to pregnancy.

Purpose: To evaluate the preconceptional health of childbearing-aged women by

examining specific health factors implicated in preterm birth in light of racial/ethnic

and socioeconomic factors. We tested the hypothesis that subgroups with historically

high levels of preterm birth would have poorer preconceptional health compared to

other groups and that the economic influence would be similar across groups.

Study Design and Methods:We performed a secondary analysis of

cross-sectional population-based data from the National Health and Nutrition

Examination Survey 20012002 and 20032004 data sets, including 1,497 of

2,108 eligible White, African American, and Mexican American women. We

measured health using select indicators of cardiovascular and metabolic disorders,

infectious disease, and sexual and substance-use behaviors associated withincreased risk for preterm birth and conducted comparisons within and across

racial groups. We used adjusted logistic regression by race.

Results:In addition to increased rates of preterm birth shown in the literature,

childbearing-aged African American women ha ve poorer overall preconceptional

health than the other groups. Measures of socioeconomic status affect

preconceptional health differently for each racial/ethnic group.

Clinical Implications:Racial/ethnic subgroups with higher rates of preterm

birth experience poorer health preconceptionally. Clinicians should address

preconceptional health risks for preterm birth in all childbearing-aged women,

paying attention to racial/ethnic-specific risks identified here.

Keywords:Ethnology; Nutrition Sur vey; Premature birth; Socioeconomic Factors;

Womens Health.

RacialChildbearing-Aged

Megan W. Arbour, PhD, CNM,Elizabeth J. Corwin, PhD, FNP,Pamela J. Salsberry, PhD, RN,Marsha Atkins, ND, CNM

DifferencesHealthofinthe

Women


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American, and Mexican American women and determineif the influence of SES on preconceptional health is similaracross the racial/ethnic subgroups. To these ends, precon-

ceptional health is measured using several indicators ofcardiovascular and metabolic disorders, infectious disease,and sexual and substance-use behaviors known both toinfluence health status and to be associated with increasedrisk of preterm birth. A womans preconceptional health isevaluated in light of race/ethnicity and SES. The hypothe-ses tested are that subgroups with known high levels ofpreterm birth (African American women) have poorer pre-conceptional health than other groups, but that the impactof SES is similar across race/ethnic groups.

Study Design and MethodsThe sample was drawn from the National Health andNutrition Examination Survey (NHANES) 20012002

and 20032004 data sets. Each NHANES is a deindenti-fied, publicly available, cross-sectional data set that esti-mates numbers and percentages of people in the UnitedStates and subgroups with diseases and risk factors. Anationally representative sample of the U.S. civiliannoninstitutionalized population is selected for eachsurvey using a complex, stratified, multistage probabilitycluster sampling design allowing results to be generalizedto the U.S. population (CDC, 2004). The data wereevaluated similarly to that described previously (Arbour,Corwin, & Salsberry, 2009). Nonpregnant MexicanAmerican, African American, and White women aged1535, with completed examination and interviewportions of the survey, were eligible for inclusion in theanalysis (n= 2,108). Sample sizes for other racial/ethnicgroups prevented their inclusion. The NHANES is nothuman subjects research and is exempt from IRB review.

Ten health variables that have an effect on various bodysystems and are known to increase the risk of pretermbirth were used to determine preconceptional health(Table 1). Although these variables are not necessarilyindependent of each other, each has been shown to inde-pendently affect birth outcome. The high-risk cut pointsidentify levels at which management often occurs clinical-ly. To determine a womans overall preconceptional healthburden, a point was assigned for each variable in the high-risk range. The health burden score was then calculatedranging from 010, with 10 indicating worse health than0. This type of score has been used previously in clinical

research studies, albeit not with these factors or populations(Martinez-Martin, 2010; McGorrian et al., 2010).

Six sociodemographic variables (age, race/ethnicity,country of birth for Mexican Americans, poverty/incomeratio (PIR), educational level, and health insurance status)were evaluated for their impact on the health burdenscore. Mexican-born immigrants demonstrate fewer pre-term births than U.S. born Mexican American, or evennon-Hispanic White women (Xiong, Buekens, Vastardis,& Wu, 2006). For this reason, country of birth for Mexi-can American women was used to examine the impact ofthis factor on preconceptional health. Families of a givensize with a higher income have a higher PIR. PIR is ranked

P

reterm birth (less than 37 weeks) occurs at arate of 1 in 8 (12.5%) of all live births in theUnited States, contributing to more than one-

third of all infant deaths during the first yearof life (March of Dimes [MOD], 2009). The incidence ofsingleton preterm birth has increased over the last decade(MOD, 2009). Healthy People 2020 recommends thatthe rate of preterm birth be less than 11.4% of all livebirths (Department of Health and Human Services[DHHS], 2011) requiring a fresh look at this epidemic.

Preterm birth does not affect all women equally. AfricanAmericans have a higher rate of preterm birth (16% to18%) than other racial/ethnic groups (11.4% in White and12.2% in Hispanic women) (Goldenberg, Culhane, Iams,& Romero, 2008; Holzman et al., 2009; MOD, 2009). Inaddition, women of low socioeconomic status (SES) are atincreased risk for preterm birth (DeFranco, Lian, Muglia,

& Schootman, 2008; Holzman et al., 2009; Haas et al.,2005). Furthermore, a previous history of preterm birth(Celik, To, Gajewska, Smith, & Nicolaides, 2008) or oneor more poor health conditions such as diabetes, hyperten-sion, iron deficiency anemia, obesity, bacterial vaginosis(BV), or periodontal disease increase the risk for pretermbirth (Catov, Nohr, Olsen, & Ness, 2008; Goldenberg etal., 2008; Haas et al., 2005; King 2006; Kramer et al.,2009; Levy, Fraser, Katz, Mazor, & Sheiner, 2005;Melamed, Melamed, Chen, Soiberman, Ben-Haroush,Hod, & Yogev et al., 2008; Radnai et al., 2004). In spite ofthis knowledge, reducing rates of preterm birth remainsdifficult. Most risk factors for preterm birth have an im-pact on a womans health preconceptionally. Therefore, toreduce the occurrence of preterm birth, healthcare provid-ers should focus their attention on preconceptional riskfactors and the health of their female clients (Freda, Moos,& Curtis, 2006; DHHS, 2011; American College of Nurse-Midwives, 2007; American College of Obstetricians andGynecologists, 2005; Johnson et al., 2006).

Reviewing the literature of the studies that examine theimpact of preconceptional health on pregnancy outcome,we found that most address only one disorder such as dia-betes, hypertension, or infection (Hitti, Nugent, Boutain,Gardella, Hillier, & Eschenbach, 2007; Paul, Boutain,Manhart, & Hitti, 2008) and do not consider the impactof multiple health conditions. One exception, Haas et al.(2005), studied the additive effect of several chronic med-ical conditions occurring preconceptionally. In this study,

women with several disorders showed increased risk forpreterm birth. These authors did not include the impact ofSES on health. Researchers also have documented the im-pact of SES (Ahern, Pickett, Selvin, & Abrams, 2003) andminority race/ethnicity on preterm birth (Rosenberg,Palmer, Wise, Horton, & Corwin, 2002), including stud-ies using the life course perspective, in which accumulatedlife stress affects pregnancy outcomes (Lu et al., 2010). Agap in the literature exists, however, as no studies to datehave shown how low SES and minority race/ethnicity af-fect preconceptional health and the risk for preterm birth.

Thus the aims of this study are to evaluate the precon-ceptional health status of childbearing-aged White, African


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data or answered dont know to any of the variables.When comparing those eligible for the analysis (nomissing data) and those ineligible using the2test, therewere no significant differences between the groups fordemographic variables at the= .05 level, and no healthburden score variables were different by race/ethnicityand income. We calculated Pearsons correlations betweenincome and education by race/ethnicity to ensure thatincome and education were not strongly correlatedin each racial/ethnic group. Although the correlationswere statistically significant at p< .05 for most groups,the correlations were small (all < 0.21). For this reason,we included both income and education in the analysis(data available on request). Prior to evaluating the pre-conceptional health of women, we calculated the preva-lence of each variable by racial/ethnic group (Table 2). Asshown, the prevalence of each variable was significantly

different for the four racial/ethnic groups at 2p < .05.For our first specific aim, to evaluate the overall health

of the different racial/ethnic groups, the mean health bur-den score for the sample was 2.40. On average, womenhad two or more health conditions. The mean score forWhite women was 2.18, for African American 2.94, forU.S. born Mexican American 2.06, and for Mexico-bornMexican American 2.37. The overall median score was2.0; Whites, U.S. born, and Mexico-born MexicanAmerican was 2.0, and African-Americans 3.0. This indi-cates that African American women have worse overallhealth with higher mean (2.94) and median (3.0) healthburden scores than White or Mexican American women.

continuously from 0 to 5 where 1 is 100% of the federalpoverty line. This is further described in Arbour et al.(2009). For the purposes of the logistic regression analy-ses, PIR was collapsed into four groups based on federalassistance eligibility criteria (USDA, 2007).

To address the first aim, health variables are comparedindividually by race/ethnicity using chi-square analysisto determine differences in health burden score. Meanand median health burden summary scores are calcu-lated for each race/ethnicity subgroup. Differencesbetween mean calculated health burden scores for eachracial/ethnic group and sociodemographic characteristicare tested using analysis of variance with = 0.05 andBonferroni correction for experimental error. Thesecond aim is addressed using adjusted odds ratios esti-mated by logistic regression. The outcome for thelogistic regression is above or below the median health

burden score. All analyses are completed using theappropriate stratum (sdmvstra), primary sampling unit(PSU) designations (sdmvpsu), and 4-year samplingweights (.5*wtmec2yr) estimated by the National Cen-ter for Health Statistics to reflect the U.S. populationbased on the 2000 census data.

ResultsThe final sample included data from 1,497 women (71%of the eligible sample), which when weighted representsthe experience of 24,276,859 U.S. women aged 15 to 35.Table 2 shows the sample attributes by race/ethnicity.Women were considered ineligible if they were missing

Table 1.Health Burden Score Variables and Their Cut-OffsSystem of

ImpactVariable High Risk Cut-off

Reference for Variable

Association With Preterm Birth

Infection/

Inflammation

Bacterial Vaginosis Positive diagnosis by Nugents Score Hitti et al., 2007; Paul et al., 2008

Trichomonas Vaginalis Positive diagnosis Hitti, et al., 2007

Periodontal Disease Clinical attachment loss or probing

depth 4 mm at one or more sites

Boggess & Edelsteink, 2006; Radnai

et al., 2004

Cardiovascular Systolic Blood Pressure >140 mm Hg or individual report of

diagnosis of high blood pressure

Catov et al., 2008

Diastolic Blood Pressure >90 mm Hg or individual report of

diagnosis of high blood pressure

Catov et al., 2008

Smoking Status Serum Cotinine >10 ng/mL or

self-report of smoking everyday

Ahern, et al., 2003

Homocysteine >8.1 mol/L Kramer et al., 2009

Iron-deficiency anemia If HGB 14.5% and

MCV < 80 fL

Levy et al., 2005

Metabolic Body Mass Index 5%a Melamed et al., 2008

aThe high risk cut point for glycated hemoglobin is >5%. A glycated hemoglobin of 5% reflects an average blood glucose level of 76120 mg/dl(Nathan et al., 2008). A glycated hemoglobin of 6% reflects an average blood glucose of 100152 mg/dl. The American Academy of Family Physi-cians notes that an individual with a glycated hemoglobin level of 6% but less than 6.5% is at great risk for developing diabetes (Mitchell, 2009).For this reason, the cut-off of greater than 5% for glycated hemoglobin may be considered conservative, but allows the health burden score tobetter reflect increased risk associated with metabolic health status.


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WhiteAfrican

American

U.S.-Born

Mexican

American

Mexico-born

Mexican

American

2p

Demographic Characteristics

Distribution 644 (43.0) 419(28.0) 262 (17.5) 172 (11.5)

Income (Poverty/

Income Ratio)

1.3 167 (24.0) 207 (47.9) 102 (38.3) 101 (52.9) 1.3 to 1.85 68 (11.8) 55 (15.6) 44 (14.9) 31 (20.4)

>1.85 to 3.5 157 (25.5) 93 (20.8) 55 (20.5) 35 (22.4)

>3.5 252 (38.6) 64 (15.6) 61 (26.3) 5 (4.4)

Education

< HSateenager 195 (16.7) 201 (18.1) 151 (26.5) 57 (9.6) HS 278 (53.3) 109 (44.0) 57 (41.1) 22 (16.6)

Health Insurance Status

Yes: private 467 (72.7) 200 (49.6) 141 (53.3) 39 (24.2)


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There are several key variables and points for

intervention for the preconceptional health of thechildbearing-aged White woman identified in this report.Consistent with the literature, White women had thehighest rates of smoking (Shavers, Lawrence, Fagan, &Gibson, 2005). Clinicians need to address smokingcessation at every visit for White women of childbearingage (U.S. Preventive Services Task Force, 2003). Whitewomen also had the highest prevalence of hyperhomocys-teinemia. One way to combat this is to increase dietaryintake of folic acid and B vitamins (Song, Cook, Alberg,Denburgh, & Manson, 2009). High rates of glycated he-moglobin >5%, abnormal body mass index (BMI) andperiodontal disease were also found in White women.

score 2 for White and Mexican American women,

but not African American women. Overall for health,education and insurance may be more important than re-sources afforded by increased income.

Clinical Nursing ImplicationsAlthough our data are cross-sectional and do not followwomen through pregnancy to determine the impact ofthe score on preterm birth occurrences, the data identifyseveral points at which change can be made to positivelyaffect the health of women preconceptionally. Women ofdifferent races/ethnicities have varied preconceptionalrisk factors for preterm birth requiring the clinician toaddress the clinical nursing implications at each visit.

Table 3.Adjusted Odds Ratios and 95% Confidence Intervals for Selected DemographicCharacteristics among Women 15-35 Years of Age for Health Burden Score2

Whitea

African Americana

Mexican Americana

Final Adjusted Modelb Final Adjusted Modelb Final Adjusted Modelb,c

Education

< HS teenager 2.34 (1.244.41) 0.54 (0.271.10) 1.12 (0.602.10)

< HS older 3.36 (0.7614.57) 3.11 (0.5019.46) 1.45 (0.533.95)

HS grad or GED 2.14 (1.064.32) 0.56 (0.261.27) 2.47 (1.195.10)

> HS ---------- ---------- ----------

Age (years)

1519 ---------- ---------- ----------

2024 1.76 (0.973.18) 1.27 (0.503.26) 1.51 (0.693.28)

2529 3.39 (1.955.90) 1.27 (0.443.68) 2.28 (1.0025.17)

3035 4.54 (2.797.40) 3.38 (0.8214.00) 4.98 (1.6514.99)

Health Insurance

Yes: private ---------- ---------- ----------

Yes: nonprivate 2.50 (1.145.52) 2.53 (0.738.73) 1.37 (0.523.64)

No 1.88 (1.043.37) 3.23 (1.437.32) 2.11 (1.024.34)

Income (Poverty/

Income Ratio)

1.3 1.31 (0.692.50) 1.25 (0.344.62) 1.71 (0.684.32)

>1.3 to 1.85 0.83 (0.411.69) 2.70 (0.6710.84) 2.29 (0.806.53)

>1.85 to 3.5 1.23 (0.712.12) 0.59 (0.181.88) 1.82 (0.655.11)

>3.5 ---------- ---------- ----------

Country of Birth

Mexico 0.69 (0.431.11)

United States ----------

aAdjusted odds ratio of Health Burden Score greater than or equal to 2.

bFinal model adjusted for age, poverty/income ratio, health insurance status, and education.

cCountry of birth (Mexico or United States) included in final model for Mexican Americans.


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High glycated hemoglobin levels and abnormal BMIsmay be managed with education about diets and exercise(Elmer et al., 2006). For periodontal disease, recommen-

dations for regular dental care should be made (Boggess& Edelstein, 2006).

African American women in this analysis had anincreased prevalence of bacterial vaginosis, trichomonasvaginalis, increased blood pressures, glycated hemoglo-bin >5%, BMI outside the normal range, and increasedprevalence of iron-deficiency anemia when compared toWhite or Mexican American women, findings that agreewith the literature (King, 2006; Killip, Bennett, & Cham-bers, 2007; Annang, Grimley, & Hook, 2006; Arbour etal., 2009; Hitti et al., 2007; Schwebke, Desmond, & Oh,2004). For intervention, African American women needtreatment and education for BV prevention, andinformation and empowerment regarding safe sexual

practices (DiClemente et al., 2009). Next, an interven-tion for lifestyle modification to reducing BMI, bloodpressure, and glycated hemoglobin is needed (Elmer etal., 2006). Finally, iron deficiency anemia may improvewith dietary counseling and addition of iron to the diet(Alleyne, Horne, & Miller, 2008).

Lastly, significantly more Mexico-born Mexican Ameri-can women had periodontal disease than the other groups,perhaps reflecting a lack of access to dental care (Xiong etal., 2006) and a need for oral care recommendations at allhealth exams (Boggess & Edelstein, 2006). Both U.S.-bornand Mexico-born Mexican American women had elevatedrates of glycated hemoglobin >5% and BMI outside thenormal range, again suggesting the need for healthy lifestyleeducation (Elmer et al., 2006). Although not shown,Mexico-born Mexican American women in the high riskanemia group had lower mean hemoglobin levels than theother groups (10.9 mg/dl in White, 10.7 mg/dl in AfricanAmerican, 11.1 in U.S.-born Mexican American, and 9.98mg/dl in Mexico-born Mexican American women).

This paper shows health differences between racial/ethnic groups and the impact of various socioeconomicvariables on health. By addressing preconceptional healthconcerns, clinicians may have the opportunity to reducethe rate of preterm birth across racial/ethnic groups.Governmental policy changes increasing education andinsurance for all may help to reduce the rate of pretermbirth by improving preconceptional health.

This study represents a novel approach examining

preconceptional health through the use of a health burdenscore, and in light of age, race/ethnicity, and SES level. How-ever, there are limitations. In our analysis, each variable ofthe health burden score was weighted equally. It might besuggested, however, that certain variables should be givengreater weight than others in light of their impact on healthoutcomes. Also, some variables may synergize with eachother to worsen health, such as diabetes mellitus and hyper-tension. The choice of cut-off thresholds chosen may also bea limitation in this study; some thresholds may be too strin-gent (e.g., glycated hemoglobin), while others may be toolax (e.g., periodontal disease). Possible underestimation ofscores due to exclusion of women with missing data is a

further limitation. Additionally, the NHANES are cross-sectional, preventing causal relationships, and this specificsample is several years old. It may not accurately reflect theimproved health of women relative to obesity preventionand other health agenda items.

Megan W. Arbour is Assistant Professor of ClinicalNursing, Coordinator of the Nurse-Midwifery Programat University of Cincinnati, Cincinnati, OH. She can bereached via e-mail at: [email protected].

Elizabeth J. Corwin is the Associate Dean forResearch at Emory Universitys Nell Hodgson WoodruffSchool of Nursing, Atlanta, GA.

Pamela J. Salsberry is a Professor at The Ohio StateUniversity College of Nursing, Columbus, OH.

Marsha Atkins is the Dean of Nursing at CuyahogaCommunity College, Cleveland, OH.

The authors declare no conflict of interest.

DOI:10.1097/NMC.0b013e31824b544e

References

Ahern, J., Pickett, K. E., Selvin, S., & Abrams, B. (2003). Preterm birthamong African American and white women: A multilevel analysisof socioeconomic characteristics and cigarette smoking.Journal ofEpidemiology & Community Health, 57(8), 606-611. doi:10.1136/

jech.57.8.606

Alleyne, M., Horne, M. K., Miller, J. L. (2008). Individualized treatmentfor iron-deficiency anemia in adults. The American Journal ofMedicine, 121(11), 943-948. doi: 10.1016/j.amjmed.2008.07.012

American College of Nurse-Midwives. (2007). Core competencies forbasic midwifery practice. Retrieved from www.midwife.org/

American College of Obstetricians and Gynecologists. (2005). The im-portance of preconception care in the continuum of womenshealth care (ACOG Committee Opinion #313). Obstetrics andGynecology, 106(3), 665-666.

Suggested Preconceptional ClinicalImplications by Race/Ethnicity

White Women

Smoking Cessation

Increased folic acid and B vitamin intake to combathyperhomocysteinemia

Lifestyle modification to reduce BMI and HgA1c

Regular dental care to prevent periodontal disease

African American Women

Treatment and prevention education for bacterialvaginosis and trichomonas vaginalis


Lifestyle changes and/or medications to normalize bloodpressure

Increased dietary iron intake to combat iron deficiency

anemia

Mexican American Women

Regular dental care to prevent periodontal disease


Increased dietary iron intake to combat iron deficiencyanemia


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& Gynecology and Reproductive Biology, 122(2), 182-186.doi:10.1016/j.ejogrb.2005.02.015

Lu, M. C., Kotelchuck, M., Hogan, V., Jones, L., Wright, K., & Halfon, N.(2010). Closing the black-white gap in birth outcomes: a life-courseapproach. Ethnicity and Disease, 20(1S2), 62-76.

March of Dimes. (2009). March of dimes, saving babies, together.Retrieved from www.marchofdimes.com

Martinez-Martin, P. (2010). Composite rating scales. Journal of theNeurological Sciences, 289(1-2), 7-11. doi:10.1016/j.jns.2009.08.013

McGorrian, C., Yusuf, S., Islam, S., Jung, H., Rangarajan, S., Avezum, A., Anand, S. (2011). Estimating modifiable coronary heart diseaserisk in multiple regions of the world: The INTERHEART ModifiableRisk Score. European Heart Journal Advance, 32(5), 581-589.doi:10.1093/eurheartj/ehq448

Melamed, N., Chen, R., Soiberman, U., Ben-Haroush, A., Hod, M., &Yogev, Y. (2008). Spontaneous and indicated preterm delivery inpregestational diabetes mellitus: etiology and risk factors. Archivesof Gynecology and Obstetrics, 278(2), 129-134. doi:10.1007/s00404-007-0541-z

Mitchell, D. (2009). Diabetes experts recommend A1c testingfor diabetes diagnosis. AAFP News Now. Retrieved from www.aafp.org/online/en/home/publications/news/news-now/clinical-care-research/20090624a1c-diab-dx.html.

Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., & Heine,R. J. (2008). Translating the A1c assay into estimated average glu-cose values. Diabetes Care, 31(8), 1473-1478. doi:10.2337/dc08-0545

Paul, K., Boutain, D., Manhart, L., & Hitti, J. (2008). Racial disparity inbacterial vaginosis: the role of socioeconomic status, psychosocialstress, and neighborhood characteristics, and possible implicationsfor preterm birth. Social Science & Medicine, 67(5), 824-833.

Radnai, M., Gorzo, I., Nagy, E., Urban, E., Novak, T., & Pal, A. (2004). Apossible association between preterm birth and early periodontitis.A pilot study. Journal of Clinical Periodontology, 31(9), 736-741.doi:10.1111/j.1600-051X.2004.00564.x

Rosenberg, L., Palmer, J. R., Wise, L. A., Horton, N. J., & Corwin, M. J.(2002). Perceptions of racial discrimination and the risk of pretermbirth.Epidemiology (Cambridge, Mass.), 13(6), 646-652.

Schwebke, J., Desmond, R., & Oh, M. (2004). Predictors of bacterialvaginosis in adolescent women who douche.Sexually TransmittedDiseases, 31(7), 433-436.

Shavers, V. L., Lawrence, D., Fagan, P., & Gibson, J. T. (2005). Racial /ethnic variation in cigarette smoking among the civilian US popula-tion by occupation and industry, TUS-CPS 1998-1999. PreventiveMedicine, 41(2), 597-606.

Song, Y., Cook, N. R., Albert, C. M., Denburgh, M. V., & Manson, J. E.(2009). Effect of homocysteine-lowering treatment with folic acidand B vitamins on risk of type 2 diabetes in women: A randomizedcontrolled trial. Diabetes, 58(8), 1921-1928. doi:10.2337/db09-0087

U.S. Department of Agriculture. (USDA) (2007). Food and NutritionService. Retrieved from www.fns.usda.gov/

U.S. Preventive Services Task Force. (2003) Counseling to preventtobacco use and tobacco-related diseases: Recommendation state-ment.Retrieved from: www.uspreventiveservicestaskforce.org/

Xiong, X., Buekens, P., Vastardis, S., & Wu, T. (2006). Periodontal diseaseas one possible explanation for the Mexican paradox. MedicalHypotheses, 67(6), 1348-1354. doi:10.1016/j.mehy.2006.05.059

Annang, L., Grimley, D., & Hook, E. W. 3rd. (2006). Vaginal douche prac-tices among black women at risk: Exploring douching prevalence,reasons for douching, and sexually transmitted disease infection.Sexually Transmitted Diseases, 33(4), 215-219. doi:10.1097/01.olq.0000205046.11916.c5

Arbour, M., Corwin, E. J., & Salsberry, P. (2009). Douching Patterns inWomen Related to Socioeconomic and Racial/Ethnic Characteris-tics. Journal of Obstetric, Gynecologic and Neonatal Nursing, 38(5),577-585. doi:10.1111/j.1552-6909.2009.01053.x

Boggess, K. A., & Edelsteink B. L. (20 06). Oral health in women duringpreconception and pregnancy: Implications for birth outcomes andinfant oral health. Maternal and Child Health Journal, 10(Suppl 5),S169-S174. doi:10.1007/s10995-006-0095-x

Buescher, P. A., & Mittal, M. (2006). Racial disparities in birth outcomesincrease with maternal age: Recent data from North Carolina.NorthCarolina Medical Journal, 67(1), 16-20.

Catov, J. M., Nohr, E. A., Olsen, J., & Ness, R. B. (2008). Chronichypertension related to risk for preterm and term small-for-gestational-age births. Obstetrics and Gynecology, 112(2 pt. 1),290-296. doi:10.1097/AOG.0b013e31817f589b.

Celik, E., To, M., Gajewska, K., Smith, G. C. S., & Nicolaides, K. H. (2008).Cervical length and obstetric history predict spontaneous pretermbirth: development and validation of a model to provide individual-ized risk assessment. Ultrasound in Obstetrics and Gynecology,

31(5), 549-554. doi:10.1002/uog.5333Centers for Disease Control and Prevention (CDC). National Center for

Health Statistics (NCHS). (2004). NHANES analytic guidelines.www.cdc.gov/

DeFranco, E. A., Lian, M., Muglia, L. A., & Schootman, M. (2008). Area-level poverty and preterm birth risk: A population-based multilevelanalysis. BMC Public Health, 8, 316. doi:10.1186/1471-2458-8-316

Department of Health and Human Services. (2011). Healthy People2020.Retrieved from www.healthypeople.gov/default.htm

DiClemente, R. J., Wingood, G. M., Rose, E. S., Sales, J. M., Lang, D. L.,Caliendo, A. M., ... Crosby, R. A. (2009). Efficacy of sexuallytransmitted disease/human immunodeficiency virus sexual risk-reduction intervention for African American adolescent femalesseeking sexual health services: A randomized controlled trial.Archives of Pediatrics and Adolescent Medicine, 163(12), 1112-1121.Retrieved from: http://archpedi.ama-assn.org/

Elmer, P. J., Obarzanek, E. , Vollmer, W. M., Simons-Morton, D., Stevens,V. J., Young, D. R., ... Ard, J. (2006). Effects of comprehensivelifestyle modification on diet, weight, physical fitness, and bloodpressure control: 18-month results of a randomized trial. Annals ofInternal Medicine, 144(7), 485-495.

Freda, M. C., Moos, M.-K., & Curtis, M. (2006). The history of preconcep-tion care: Evolving guidelines and standards. MCN The AmericanJournal of Maternal Child Nursing, 10(S1), 43-52. doi:10.1007/s10995-006-0087-x

Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Pre-term birth 1: Epidemiology and causes of preterm birth. The Lancet,371, 7584. Retrieved from www.thelancet.com

Haas, J. S., Fuentes-Afflick, E., Stewart, A. L., Jackson, R. A., Dean, M.L., Brawarsky, P., Escobar, G. J. (2005). Prepregnancy health statusand the risk of preterm delivery.Archives of Pediatrics & Adoles-cent Medicine, 159(1), 58-63.

Hitti, J., Nugent, R., Boutain, D., Gardella, C., Hillier, S. L., & Eschenbach,D. A. (2007). Racial disparity in risk of preterm birth associated withlower genital tract infection.Paediatric and Perinatal Epidemiology,21(4), 330-337. doi:10.1111/j.1365-3016.2007.00807.x

Holzman, C., Eyster, J., Kleyn, M., Messer, L. C., Kaufman, J. S., Laraia,B. A., ... Elo, I. T. (2009). Maternal weathering and risk of pretermdelivery. American Journal of Public Health, 99(10), 1864-1871. doi:

10.2105/AJPH.2008.151589Johnson, K., Posner, S. F., Biermann, J., Cordero, J. F., Atrash, H. K.,

Parker, C. S., ... Curtis, M. G. (2006). Recommendations to improvepreconception health and health careUnited States. MMWRMorbidity and Mortali ty Weekly Report, 55(RR-6), 1-23. www.cdc.gov

Killip, S., Bennett, J. M., & Chambers, M. D. (2007). Iron deficiencyanemia.American Family Physician, 75(5), 671-678.

King, J. C. (2006). Maternal obesity, metabolism, and pregnancyoutcomes. Annual Review of Nutrition, 26, 271-291. doi:10.1146/annurev.nutr.24.012003.132249

Kramer, M. S., Kahn, S. R., Rozen, R., Evans, R., P, Platt, R. W., Chen, M.F., ... Genest J. (2009). Vasculopathic and thrombophilic risk factorsfor spontaneous preterm birth. International Journal of Epidemiol-ogy, 38(3), 715-723. doi: 10.1093/ije/dyp167

Levy, A., Fraser, D., Katz, M., Mazor, M., & Sheiner, E. (2005). Maternalanemia during pregnancy is an independent risk factor for lowbirthweight and preterm delivery. European Journal of Obstetrics

American College of Nurse-Midwives:www.midwife.org

American Congress of Obstetricians

and Gynecologists:www.acog.org/

AWHONN: Association of WomensHealth, Obstetric and Neonatal Nurses:www.awhonn.org/awhonn/

CDC: Preconception Care:www.cdc.gov/ncbddd/preconception/

Healthy People 2020: Maternal, Infant, and childHealth:www.healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=26

March of Dimes:www.marchofdimes.com/

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