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January 2005

Prepregnancy Health Status and the Risk of Preterm Delivery

Author Affiliations

Author Affiliations: Division of General Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Mass (Dr Haas and Ms Brawarsky); Department of Pediatrics (Dr Fuentes-Afflick), Institute for Health and Aging (Dr Stewart), Departments of Obstetrics, Gynecology, and Reproductive Sciences and Epidemiology and Biostatistics (Dr Jackson), and Institute for Health Policy Studies (Ms Dean), University of California, San Francisco; and Perinatal Research Unit, Kaiser Permanente Medical Care Program, Division of Research, Oakland, Calif (Dr Escobar).

Arch Pediatr Adolesc Med. 2005;159(1):58-63. doi:10.1001/archpedi.159.1.58

Background  Despite extensive evaluation, our understanding of risk factors for premature delivery is incomplete.

Objective  To examine whether a woman’s health status and risk factors before pregnancy are associated with a woman’s risk of preterm delivery, independent of risk factors that occur during pregnancy.

Design, Setting, and Participants  Prospective cohort of pregnant women in the San Francisco Bay area who delivered a singleton infant (n = 1619).

Main Outcome Measure  Preterm delivery (<37 weeks’ gestational age).

Results  Sociodemographic characteristics alone explained 13.0% of the risk of preterm delivery, whereas risk factors that occurred before pregnancy explained 39.8% and risk factors that occurred during pregnancy explained 47.1%. After we adjusted for sociodemographic characteristics, prepregnancy risk factors, and pregnancy risk factors, women who reported poor physical function during the month before conception were nearly twice as likely to experience a preterm delivery (odds ratio, 1.97; 95% confidence interval, 1.18-3.30) as women with better physical function.

Conclusion  A broader focus on the health of women prior to pregnancy may improve rates of preterm delivery.

Preterm delivery is one of the most important causes of perinatal morbidity and mortality and childhood morbidity in the United States.1,2 In addition, ethnic disparities in prematurity are a significant public health problem.2,3 The incidence of preterm births has risen over the last 15 years and is at least twice as high among African American women as among white women.2,3 Reducing disparities in prematurity has been identified as a national public health priority.4

For these reasons, risk factors for preterm delivery have been extensively examined, and several have been identified, including maternal age,5 socioeconomic status,6,7 marital status,8,9 parity,10 smoking,11 mental health,12,13 alcohol and substance use,14 the adequacy of prenatal care,15,16 physical activity,17-19 chronic illness (eg, asthma20-23 and diabetes24,25), and intrauterine infection.26-28 Despite such extensive evaluation, our understanding of the etiology of preterm delivery remains incomplete. Similarly, interventions designed to reduce preterm delivery have focused on the prenatal period. For example, the expansion of public health coverage for pregnant women was implemented to improve access to prenatal care and reduce rates of prematurity. Unfortunately, these programs have had limited effect on these outcomes.29-31

These findings suggest that our focus on the prenatal period may be too narrow. By the time a woman is pregnant, it may be too late to modify important health behaviors, treat chronic illness, or improve her health status. A broader perspective on a woman’s health status before pregnancy may be necessary to improve our understanding of the factors associated with preterm delivery. The goal of this study was to examine the relationship between a woman’s prepregnancy health status and her risk of subsequent preterm delivery.



Project WISH (Women and Infants Starting Healthy) is a longitudinal cohort of pregnant women who received their prenatal care at a practice or clinic affiliated with 1 of 6 delivery hospitals in the San Francisco Bay area. Women were eligible to participate in Project WISH if they (1) received prenatal care at 1 of the practices or clinics associated with these delivery hospitals and planned to deliver at 1 of these hospitals; (2) were at least 18 years old at the time of recruitment; (3) spoke English, Spanish, or Cantonese; (3) sought prenatal care before 16 weeks’ gestational age; and (4) could be contacted by telephone.

Potentially eligible women were sent an informational letter explaining the study and requesting their participation. This mailing included a prestamped, preaddressed “opt-out” postcard that a woman could return if she did not wish to be contacted. If no “opt-out” postcard was returned within 2 weeks of the mailing, attempts were made to contact the woman by telephone. When a woman was reached, verbal informed consent was obtained using a standard script. Women were enrolled between May 2001 and July 2002. The research protocol was reviewed and approved by the institutional review boards of the participating institutions.


Women who agreed to participate were asked to complete 4 telephone surveys: (1) before 20 weeks’ gestation, (2) at 24 to 28 weeks’ gestation, (3) at 32 to 36 weeks’ gestation, and (4) 8 to 12 weeks’ postpartum. During each interview, women were asked to report their physical functioning using the Medical Outcome Study (MOS) Short Form-36 (SF-36).32 The MOS SF-36 contains subscales to measure 8 dimensions of health, including physical function, physical role function, bodily pain, general health, vitality, social function, emotional role function, and mental health. This instrument has been extensively evaluated in many populations and has been shown to have adequate psychometric properties, including validity and reliability. Scores for each subscale range from 0 to 100, with a higher score indicating better function. During each interview, women also were asked to complete the short-form Center for Epidemiologic Studies–Depression Scale (CES-D) to screen for depressive symptoms.33 During the first interview (median gestational age at the time of this interview, 16 weeks), participants were asked to report their health status, using the MOS SF-36 and the CES-D, during the month before they became pregnant. During subsequent interviews, they were asked to report their health status during the prior 4 weeks.

Other information ascertained in the first interview included age, race, ethnicity, country of birth, marital status, education, prepregnancy chronic medical conditions (including anemia, asthma or other chronic lung diseases, diabetes, hypertension, epilepsy, HIV, cancer, and rheumatologic, thyroid, kidney, liver, and heart disease), parity, weight and height during the month before pregnancy, the frequency and duration of exercise during the month before pregnancy, and tobacco use during the 3 months before conception and during pregnancy. Additional data collected during the subsequent pregnancy interviews included the diagnosis or treatment of obstetric conditions during pregnancy (including preeclampsia or eclampsia, gestational diabetes, placenta previa, placental abruption, oligohydramnios, polyhydramnios, intrauterine growth restriction, isoimmunization, incompetent cervix, and current smoking). Following delivery, the medical records of women and their infants were reviewed to obtain data about the length of gestation; birth weight; the adequacy of prenatal care34 ; the use of tobacco, alcohol, and illicit drugs; and the chronic medical conditions and pregnancy-associated conditions previously noted.


Preterm delivery (<37 weeks’ gestational age at the time of delivery) was the outcome variable for these analyses. Independent variables included self-reported age (categorized in 5-year intervals), self-reported race/ethnicity (white, Latina, African American, and Asian American), self-reported country of birth (United States–born vs other), self-reported marital status (married or living with a partner vs other family structures), self-reported level of education (less than high school, high school graduate or some college, college graduate or higher), parity recorded in the medical record (nulliparous vs parous), self-reported exercise prior to pregnancy (none vs some), prepregnancy body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) as recorded in the medical record or by self-report if the chart data were not available (BMI: underweight, <18.5 kg/m2; normal, 18.5 kg/m2 to 24.9 kg/m2; overweight, 25 kg/m2 to 29.9 kg/m2 and >30 kg/ m2), the chronic medical conditions previously listed if noted in the medical record or by self-report, the pregnancy-associated conditions previously listed if noted in the medical record or by self-report, smoking status both before pregnancy and during pregnancy based on medical record review or self-report, and the adequacy of prenatal care based on medical record review.34 A woman was considered to have depressive symptoms if her score on the short-form version of the CES-D was higher than 10, consistent with the definition for this version of the instrument.33


This analysis was restricted to women with a singleton delivery. Variables for the multivariate models were selected on the basis of a priori hypotheses or bivariate associations. The perinatal health framework was used as a conceptual model for these analyses.35 This framework recognizes the influence of distal determinants of health, including demographic and socioeconomic characteristics; proximal determinants, including biomedical conditions and behavioral factors; and the interface between the preconceptional and the prenatal periods. We therefore examined a series of 3 multivariate logistic regression models: (1) a model that examined only sociodemographic characteristics (age, race, ethnicity, place of birth, level of education, and parity); (2) a model that included sociodemographic determinants plus prepregnancy health status and risk factors (prepregnancy BMI, physical function, depressive symptoms, chronic medical conditions, smoking, and exercise); and (3) a model that also included risk factors that occurred during pregnancy (adequacy of prenatal care, physical function during pregnancy, depressive symptoms during pregnancy, pregnancy-associated conditions, smoking during pregnancy, and use of illicit drugs during pregnancy). We then estimated the relative contribution of sociodemographic, prepregnancy, and pregnancy factors to the variation in premature birth by the marginal change in the model χ2 as each group of factors as it was added and removed from the full model.36 The c statistic is a measure of the concordance between the predicted and observed outcomes. Values closer to 1 indicate a better model fit.37

Because many of the women were at the upper limit of the physical function scale before pregnancy, we chose to examine poor function, defined as below the 25th percentile for women of reproductive age from national normative data.32 This represented a physical function score of 85 or lower. Women with a score above this threshold were considered to have normal function. Because a greater range of physical function was reported during pregnancy, physical function during pregnancy was analyzed as a continuous score. Multivariate models also accounted for the site of care.


Response and retention rates

Of the 2854 women who were potentially eligible to participate in this study, 1809 participated and 1045 refused (actively or passively) for a response rate of 63%. Of these women, 1648 (91%) subsequently delivered an infant at 1 of the participating hospitals (the remainder had a spontaneous abortion or therapeutic abortion or delivered elsewhere). Twenty-three women with a multiple-gestation birth and 6 women who described their race and ethnicity as Native American were excluded from this analysis, leaving a sample of 1619. Of these women, 1581 (98%) completed subsequent surveys during pregnancy. Respondents at baseline and nonrespondents were of similar age. Nonrespondents were more likely (P<.05) than respondents to be Asian American (23.9% vs 14.5%) and less likely to be African American (11.3% vs 18.3%), Latina (30.5% vs 35.3%), or white (20.6% vs 31.6%).

Characteristics of the sample

The rate of preterm delivery was 8.0% (Table 1). The majority of women had a prior pregnancy, were born in the United States, and were married or living with a partner. The cohort was ethnically and socioeconomically diverse. Approximately one quarter of the sample did not exercise prior to pregnancy. In unadjusted analyses, African American women were more likely than white women to have a preterm delivery. Education, BMI, tobacco use, chronic medical problems prior to pregnancy, pregnancy complications, poor physical function prior to pregnancy, and depressive symptoms prior to or during pregnancy were each associated with the unadjusted odds of preterm delivery.

Table 1. 
Description of the Sample (n=1619)*
Description of the Sample (n=1619)*

Risk factors for preterm delivery

After adjustment for age and other sociodemographic characteristics (Table 2, Model 1), African American women were more likely than white women to experience a preterm delivery (odds ratio [OR], 1.94; 95% confidence interval [CI], 1.07-3.51). There were no significant differences between whites and Latinas or Asian Americans in the rate of preterm delivery. After adjustment for prepregnancy risk factors (Model 2), the difference in the risk of preterm delivery between African American and white women diminished in magnitude (OR, 1.72; CI, 0.93-3.17). Several indicators of prepregnancy health status were associated with an increased risk of preterm delivery, including underweight BMI (OR, 2.38; CI, 1.04-5.48), a history of chronic hypertension (OR, 3.12; CI, 1.94-5.02), poor prepregnancy physical function (OR, 2.31; CI, 1.41-3.77), and smoking before pregnancy (OR, 2.20; CI, 1.29-3.75). Depressive symptoms before pregnancy were not associated with the risk of preterm delivery. Most of these prepregnancy factors remained significantly associated with the risk of preterm delivery after adjusting for risk factors that occurred during pregnancy (Model 3). Smoking, pregnancy-associated hypertension, and other pregnancy-associated complications were significantly associated with an increased risk of preterm delivery.

Table 2. 
Factors Associated With the Risk of Preterm Birth*
Factors Associated With the Risk of Preterm Birth*

Using the model that included demographic, prepregnancy, and pregnancy factors (Model 3), we estimated the relative contributions of each group of factors to the variation in the risk of preterm delivery: demographic characteristics alone contributed 13.0% of the variation in the risk of preterm delivery, whereas prepregnancy characteristics contributed 39.8% and risk factors that occurred during pregnancy contributed 47.1%.


The results of this large prospective study offer a broader perspective on maternal risk factors for preterm delivery. We observed that several prepregnancy risk factors were as important as pregnancy-specific risk factors in explaining a woman’s risk of preterm delivery. While prenatal care and other interventions during pregnancy can address conditions that occur during pregnancy, they are not designed to address the cumulative burden of poor physical function before pregnancy. Interventions and policies directed at improving access to care during pregnancy may fall short of the goal of reducing preterm delivery because they cannot address this legacy of poor health status and health behaviors.

Other studies support our finding that it is important to consider a broader perspective of risk factors for preterm delivery. Our finding that a low prepregnancy BMI is associated with an increased risk of preterm delivery, perhaps related to inadequate nutritional status, has been previously observed.38 Women who themselves were of low birth weight are at an increased risk for having a low-birth-weight infant.39 Although the factors that mediate this continuity of risk are unknown, low birth weight is associated with the subsequent development of impaired glucose tolerance and hypertension in adulthood,40,41 and both of these conditions are risk factors for adverse birth outcomes.24,25

In addition to examining the association of chronic conditions and health behaviors with the risk of preterm delivery, we examined patient-reported measures of physical function and depressive symptoms. Patient-reported measures of health status are increasingly being incorporated into clinical care and research and were developed to complement more traditional provider-determined physiologic and clinical outcomes.42-44 The results of the current study extend our earlier finding that poor self-reported physical function is associated with the occurrence of preterm labor.45 Conversely, report of depressive symptoms before or during pregnancy was not associated with the risk of preterm delivery. Further work should consider whether self-reported physical function could be used prospectively to identify women who would benefit from preconception interventions to prevent prematurity.

Our findings have several potential limitations. First, women retrospectively recalled their prepregnancy health status during early pregnancy. Importantly, this information was obtained early in pregnancy, well before delivery. The prepregnancy health status reported by these women was, if anything, better than that reported in normative data for women of reproductive age.32 In addition, the retrospective recall of these health-status measures have been shown to be reliable during a 1-year period.46 Second, the associations observed are not causal. Both prepregnancy health status and the risk of preterm delivery may be associated with an earlier exposure, perhaps in utero.40,47 Third, the initial response rate of 63% may limit the generalizability of our findings. This response rate is similar to that observed in other pregnancy cohorts.48 Retention in our cohort was very high. Finally, we only examined maternal risk factors that occurred during the immediate preconception period. Future work should examine the role of maternal risk factors from a life-course perspective.35

These findings suggest that interventions to reduce the rate of preterm delivery may need to start before pregnancy. Presently, preconception care is strongly endorsed only for women with specific medical conditions, notably diabetes.49 Since almost half of the pregnancies in the United States are unintended,50 improving birth outcomes may require improving the health status of all women without regard to their plans for conception. Several studies have examined the effect of public health insurance programs that provide health care coverage during pregnancy. While some of these studies demonstrate an increase in enrollment,51 the effects of these programs on birth outcomes have been limited.29-31 The care of women of reproductive age in the United States is fragmented; women generally seek care for family planning, obstetrics, and general medical care from different providers, often with poor coordination of care.52 The provision of health insurance coverage to all women of reproductive age, for example, may result in greater improvements in the rate of preterm delivery than coverage directed at women only once they become pregnant. Sweden’s universal health care access program, for example, has been associated with fewer social disparities in birth outcomes.53

In conclusion, this study suggests that maternal health status prior to pregnancy is associated with the risk of preterm delivery. Improving the rates of preterm delivery may require attention to the health status of women before pregnancy.

Correspondence: Jennifer S. Haas, MD, MSPH, Division of General Medicine and Primary Care, Brigham and Women’s Hospital, 1620 Tremont St, Boston, MA 02120-1613 (

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Article Information

Accepted for Publication: August 9, 2004.

Funding/Support: This study was supported by grant R01 HD37389 from the National Institute of Child Health and Human Development, Bethesda, Md.

Acknowledgment: We thank Dawn P. Misra, PhD, for thoughtful comments on an earlier version of this article.

Arias  EMacDorman  MFStrobino  DMGuyer  B Annual summary of vital statistics, 2002.  Pediatrics 2003;1121215- 1230PubMedGoogle ScholarCrossref
Demissie  KRhoads  GGAnanth  CV  et al.  Trends in preterm birth and neonatal mortality among blacks and whites in the United States from 1989 to 1997.  Am J Epidemiol 2001;154307- 315PubMedGoogle ScholarCrossref
Goldenberg  RLRouse  DJ Prevention of premature birth.  N Engl J Med 1998;339313- 320PubMedGoogle ScholarCrossref
US Department of Health and Human Services, Healthy People 2010.  Washington, DC US Dept of Health and Human Services2000;
Fraser  AMBrockert  JEWard  RH Association of young maternal age with adverse reproductive outcomes.  N Engl J Med 1995;3321113- 1117PubMedGoogle ScholarCrossref
Lieberman  ERyan  KJMonson  RRSchoenbaum  SC Risk factors accounting for racial differences in the rate of premature birth.  N Engl J Med 1987;317743- 748PubMedGoogle ScholarCrossref
Shiono  PHKlebanoff  MA Ethnic differences in preterm and very preterm delivery.  Am J Public Health 1986;761317- 1321PubMedGoogle ScholarCrossref
Hein  HABurmeister  LFPapke  KR The relationship of unwed status to infant mortality.  Obstet Gynecol 1990;76763- 768PubMedGoogle ScholarCrossref
Bennett  T Marital status and infant health outcomes.  Soc Sci Med 1992;351179- 1187PubMedGoogle ScholarCrossref
Kramer  MS Determinants of low birth weight: methodological assessment and meta-analysis.  Bull World Health Organ 1987;65663- 737PubMedGoogle Scholar
Ellard  GAJohnstone  FDPrescott  RJJi-Xian  WJian-Hua  M Smoking during pregnancy.  Br J Obstet Gynaecol 1996;103806- 813PubMedGoogle ScholarCrossref
Copper  RLGoldenberg  RLDas  A  et al.  The preterm prediction study: maternal stress is associated with spontaneous preterm birth at less than thirty-five weeks’ gestation: National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network.  Am J Obstet Gynecol 1996;1751286- 1292PubMedGoogle ScholarCrossref
Zuckerman  BAmaro  HBauchner  HCabral  H Depressive symptoms during pregnancy: relationship to poor health behaviors.  Am J Obstet Gynecol 1989;1601107- 1111PubMedGoogle ScholarCrossref
Lundsberg  LSBracken  MBSaftlas  AF Low-to-moderate gestational alcohol use and intrauterine growth retardation, low birthweight, and preterm delivery.  Ann Epidemiol 1997;7498- 508PubMedGoogle ScholarCrossref
Showstack  JABudetti  PPMinkler  D Factors associated with birthweight.  Am J Public Health 1984;741003- 1008PubMedGoogle ScholarCrossref
Kogan  MDAlexander  GRKotelchuck  MNagey  DA Relation of the content of prenatal care to the risk of low birth weight.  JAMA 1994;2711340- 1345PubMedGoogle ScholarCrossref
Luke  BMamelle  NKeith  L  et al.  The association between occupational factors and preterm birth: a United States nurses’ study.  Am J Obstet Gynecol 1995;173849- 862PubMedGoogle ScholarCrossref
Ramirez  GGrimes  RMAnnegers  JFDavis  BRSlater  CH Occupational physical activity and other risk factors for preterm birth among US Army primigravidas.  Am J Public Health 1990;80728- 730PubMedGoogle ScholarCrossref
Misra  DPStrobino  DMStashinko  EENagey  DANanda  J Effects of physical activity on preterm birth.  Am J Epidemiol 1998;147628- 635PubMedGoogle ScholarCrossref
Kramer  MSCoates  ALMichoud  MC  et al.  Maternal asthma and idiopathic preterm labor.  Am J Epidemiol 1995;1421078- 1088PubMedGoogle Scholar
Sorensen  TKDempsey  JCXiao  RFrederick  IOLuthy  DAWilliams  MA Maternal asthma and risk of preterm delivery.  Ann Epidemiol 2003;13267- 272PubMedGoogle ScholarCrossref
Wen  SWDemissie  KLiu  S Adverse outcomes in pregnancies of asthmatic women: results from a Canadian population.  Ann Epidemiol 2001;117- 12PubMedGoogle ScholarCrossref
Tan  KSThomson  NC Asthma in pregnancy.  Am J Med 2000;109727- 733PubMedGoogle ScholarCrossref
Sibai  BMCaritis  SNHauth  JC  et al.  Preterm delivery in women with pregestational diabetes mellitus or chronic hypertension relative to women with uncomplicated pregnancies: the National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network.  Am J Obstet Gynecol 2000;1831520- 1524PubMedGoogle ScholarCrossref
Sibai  BMCaritis  SHauth  J  et al.  Risks of preeclampsia and adverse neonatal outcomes among women with pregestational diabetes mellitus: National Institute of Child Health and Human Development Network of Maternal-Fetal Medicine Units.  Am J Obstet Gynecol 2000;182364- 369PubMedGoogle ScholarCrossref
Hauth  JCGoldenberg  RLAndrews  WWDuBard  MBCopper  RL Reduced incidence of preterm delivery with metronidazole and erythromycin in women with bacterial vaginosis.  N Engl J Med 1995;3331732- 1736PubMedGoogle ScholarCrossref
Goldenberg  RLAndrews  WWYuan  ACMacKay  HTSt Louis  ME Sexually transmitted diseases and adverse outcomes of pregnancy.  Clin Perinatol 1997;2423- 41PubMedGoogle Scholar
Goldenberg  RLHauth  JCAndrews  WW Intrauterine infection and preterm delivery.  N Engl J Med 2000;3421500- 1507PubMedGoogle ScholarCrossref
Piper  JMRay  WAGriffin  MR Effects of Medicaid eligibility expansion on prenatal care and pregnancy outcome in Tennessee.  JAMA 1990;2642219- 2223PubMedGoogle ScholarCrossref
Haas  JSUdvarhelyi  ISMorris  CNEpstein  AM The effect of providing health coverage to poor uninsured pregnant women in Massachusetts.  JAMA 1993;26987- 91PubMedGoogle ScholarCrossref
Ray  WAGigante  JMitchel  EF  JrHickson  GB Perinatal outcomes following implementation of TennCare.  JAMA 1998;279314- 316PubMedGoogle ScholarCrossref
Ware  JE  Jr SF-36 Health Survey: Manual and Interpretation Guide.  Boston, Mass The Health Institute1993;Available at: October 15, 2004
Andresen  EMMalmgren  JACarter  WBPatrick  DL Screening for depression in well older adults.  Am J Prev Med 1994;1077- 84PubMedGoogle Scholar
Kotelchuck  M An evaluation of the Kessner Adequacy of Prenatal Care Index and a proposed Adequacy of Prenatal Care Utilization Index.  Am J Public Health 1994;841414- 1420PubMedGoogle ScholarCrossref
Misra  DPGuyer  BAllston  A Integrated perinatal health framework: a multiple determinants model with a lifespan approach.  Am J Prev Med 2003;2565- 75PubMedGoogle ScholarCrossref
Render  MLKim  HMWelsh  DE  et al.  Automated intensive care unit risk adjustment: results from a National Veterans Affairs study.  Crit Care Med 2003;311638- 1646PubMedGoogle ScholarCrossref
Katz  MH Multivariable Analysis: A Practical Guide for Clinicians.  Cambridge, England Cambridge University Press1999;
Hickey  CACliver  SPMcNeal  SFGoldenberg  RL Low pregravid body mass index as a risk factor for preterm birth.  Obstet Gynecol 1997;89206- 212PubMedGoogle ScholarCrossref
Wang  XZuckerman  BCoffman  GACorwin  MJ Familial aggregation of low birth weight among whites and blacks in the United States.  N Engl J Med 1995;3331744- 1749PubMedGoogle ScholarCrossref
Paneth  N The impressionable fetus? fetal life and adult health.  Am J Public Health 1994;841372- 1374PubMedGoogle ScholarCrossref
Rich-Edwards  JWStampfer  MJManson  JE  et al.  Birth weight and risk of cardiovascular disease in a cohort of women followed up since 1976.  BMJ 1997;315396- 400PubMedGoogle ScholarCrossref
Tarlov  ARWare  JE  JrGreenfield  SNelson  ECPerrin  EZubkoff  M The Medical Outcomes Study: an application of methods for monitoring the results of medical care.  JAMA 1989;262925- 930PubMedGoogle ScholarCrossref
Calkins  DRRubenstein  LVCleary  PD  et al.  Failure of physicians to recognize functional disability in ambulatory patients.  Ann Intern Med 1991;114451- 454PubMedGoogle ScholarCrossref
Ruo  BRumsfeld  JSHlatky  MALiu  HBrowner  WSWhooley  MA Depressive symptoms and health-related quality of life: the Heart and Soul Study.  JAMA 2003;290215- 221PubMedGoogle ScholarCrossref
Haas  JSMeneses  VMcCormick  MC Outcomes and health status of socially disadvantaged women during pregnancy.  J Womens Health Gend Based Med 1999;8547- 553PubMedGoogle ScholarCrossref
Perneger  TVEtter  JFRougemont  A Prospective versus retrospective measurement of change in health status: a community-based study in Geneva, Switzerland.  J Epidemiol Community Health 1997;51320- 325PubMedGoogle ScholarCrossref
Barker  DJForsen  TUutela  AOsmond  CEriksson  JG Size at birth and resilience to effects of poor living conditions in adult life: longitudinal study.  BMJ 2001;3231273- 1276PubMedGoogle ScholarCrossref
Oken  EKleinman  KPBerland  WESimon  SRRich-Edwards  JWGillman  MW Decline in fish consumption among pregnant women after a national mercury advisory.  Obstet Gynecol 2003;102346- 351PubMedGoogle ScholarCrossref
Gold  AEReilly  RLittle  JWalker  JD The effect of glycemic control in the pre-conception period and early pregnancy on birth weight in women with IDDM.  Diabetes Care 1998;21535- 538PubMedGoogle ScholarCrossref
Henshaw  SK Unintended pregnancy in the United States.  Fam Plann Perspect 1998;3024- 29,46PubMedGoogle ScholarCrossref
Braveman  PBennett  TLewis  CEgerter  SShowstack  J Access to prenatal care following major Medicaid eligibility expansions.  JAMA 1993;2691285- 1289PubMedGoogle ScholarCrossref
Meade  V Integrating women’s healthcare-serving the whole woman.  Qual Lett Healthc Lead 1997;92- 10PubMedGoogle Scholar
Hogue  CJHargraves  MA Class, race, and infant mortality in the United States.  Am J Public Health 1993;839- 12PubMedGoogle ScholarCrossref