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Sot. Sci. Med. Vol. 25, No. 12, pp. 1321-1327, 1987 0277-9536/87 3.00 + 0.00Printed in Great Britain All rights reserved Copyright 0 1987 Pergamon Journals Ltd

PREDICTING LOW BIRTHWEIGHT AND

COMPLICATED LABOR IN URBAN BLACK

WOMEN A BIOPSYCHOSOCIAL PERSPECTIVE

KENNETH G. REEB,* ANT~NNETIE V. GRAHAM, STEPHEN J. ZYZANSKI and GAY C. KIT~ONDepartments of Family Medicine and Anthropology, Case Western Reserve University and University

Hospitals of Cleveland, Cleveland, Ohio, U.S.A.

Abstract-This study explored demographic, biomedical and psychosocial factors as predictors of twoadverse pregnancy outcomes: intrapartum complications and low birthweight, in 140 urban blackpregnant women. The intrapartum complication rate was 18%. A four factor equation (low familyfunctioning, advanced maternal age, working during pregnancy, and short stature) predicted intrapartumcomplications (80% sensitivity, 67% specificity and 35% positive predictive value). The low birthweightrate was 14%. Four factors (low family functioning, stressful events, Quetelet’s Index, and cigarettesmoking) predicted low birthweight (65% sensitivity, 84% specificity and 42% positive predictive value).Family functioning, alone, predicted low birthweight with 65% sensitivity, 64% specificity and 31%positive predictive value. Family functioning, was the only predictor for both outcomes. Familyfunctioning and other psychosocial risk factors may potentially improve identification of high riskpregnant urban black women.

Key words-prenatal screening, risk assessment, low birthweight, family functioning, stress

INTRODUCHON

The early identification of pregnant women at highrisk for intrapartum complications (IPC) and fordelivery of low birthweight (LBW) infants is a pre-requisite for the development of cost effective preven-

tive obstetrics programs [l, 21. Such programs arebadly needed to reduce prematurity and other ad-verse pregnancy outcomes (APO). Low birthweightinfants bear a disproportionate risk for neonatalmortality and morbidity; the LBW and infant mor-tality rates for U.S. blacks are approximately twicethose for whites [3]. The World Health Organizationrecently acknowledged the worldwide importance ofprematurity by designating the LBW rate as a globalindicator of health [4]. Primary care providers mustbe able to identify high risk women who will benefitfrom preventive services, consultation or referral toappropriate tertiary care obstetrical centers [5].

The usefulness of currently available prenatal riskscoring systems (RSS) is limited. Their specificity andsensitivity is generally low [6,7]. Improving the per-formance of such measures is difficult because of therelatively infrequent occurrence of most major medi-cal risk factors for prematurity, such as severe pre-eclampsia and placenta previa [8]. Most availableRSS focus primarily on biomedical factors, oftenassuming that psychosocial information, if indeedpertinent, is difficult for physicians to obtain andinterpret and, furthermore, is considered beyond thescope of present day obstetrics [9]. However, the

*Correspondence should be addressed to: Dr Kenneth G.Reeb, Chairman, Department of Family Medicine,Campus Box 7595-Gravely Building, University ofNorth Carolina at Chapel Hill, Chapel Hill, NC 27599,U.S.A.

Presented, in part, at North American primary CareResearch Group Annual Meeting, Seattle, Washington,April, 1985.

etiology of adverse pregnancy outcomes is probablymultifactorial with a biomedical, psychological andsocioeconomic synergism between stressors [10, 111.The stress response becomes pathogenic, especially inwomen with inadequate familial or interpersonalsupport or other resources which otherwise mighthelp them to cope (12, 131. Most risk scoring systemsdo not specifically screen for stress or inadequatesupport, however.

Present RSS and premature birth prevention pro-grams for urban black women in the United Stateshave limited effectiveness. These women have anoverall higher risk for prematurity and other adversepregnancy outcomes [14]. Any national program toimprove perinatal health should include the devel-opment of more effective prenatal risk screening andpreventive services for black women.

This study explores the effectiveness of a practicalRSS, for use with pregnant urban black women,

which combines psychosocial and biomedical factors,as predictors of two important types of adversepregnancy outcomes: intrapartum complications(IPC) and low birthweight (LBW) infants. It ad-dresses three related research questions: (1) Whichprenatal medical and psychosocial factors correlatebest with IPC and with LBW in this sample of urbanblack pregnant women? (2) gow well do profiles ofindependent prenatal factors predict IPC and LBWin this population? (3) To what extent do prenatalpsychosocial factors account for these two adversepregnancy outcomes independent of known bio-medical risk factors?

METHODS

Sample

A consecutive sample of 140 black pregnantwomen in their seventh month of gestation was

1321

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1322 KENNETH G. F EB et al.

recruited between May 1982 and August 1983. Allwomen were patients of an urban university hospital-based family practice center located in Cleveland,Ohio. Patients are self-referred to this primary carefacility. Seventy-six women were patients prior to thispregnancy. Of the 64 women who joined the practicewhile pregnant, 47 (34% of total sample) beganprenatal care after their four month of gestation.Women attend the center for their prenatal carewhich is provided by family physicians with on-siteobstetrical consultation available. Patients judged tobe at obstetrical risk are referred to the high riskobstetrics clinic. (Four subjects received some highrisk services.) Intrapartum care was provided on theteaching hospital’s obstetrical service. Informed con-sent was obtained prior to a structured interview bya trained, mature black woman interviewer. Prenataland intrapartum clinical data were obtained inroutine clinical fashion and were recorded on the

Cleveland Regional Perinatal Network Prenatal RiskScoring Forms adopted from Hobel [I 51. Of the 146eligible women contacted, 140 agreed to participatefor a 96% response rate.

The subjects’ average age was 24 years with 18%in the 14-19 year group and 6% 35 years and older.Twenty-seven percent were primagravidas. Eighty-one percent were in the two lowest socioeconomicgroups, using Hollingshead’s Index of Social Position[16]; they were equally distributed with 40.5% each inGroups IV and V. Thirty-four percent were marriedand an additional 16% unmarried but living with apartner; thus, 50% of subjects were living with a malepartner.

Variables Analysis

Dependent study variables include: (1) intrapartumcomplications and (2) low birthweight infants. Intra-partum complications cases are operationally definedas having three or more significant problems occur-ring during labor and delivery, as recorded on theintrapartum risk form of the Cleveland RegionalPerinatal Network (CRPN) risk scoring system adop-ted from Hobel [15]. Severity of intrapartum prob-lems was judged independently by two clinicians asbeing sufficient to warrant management in a high-risk

obstetrical unit. Twenty-five IPC cases (18% of 136)were identified in this sample.

Low birthweight infants are those infants born inthe study weighing 2500 g or less, regardless of theirgestational age. Twenty LBW infants (14% of 139)were delivered in this study. Missing data on one ormore variables for ten subjects limited final analysisto a sample of 130 subjects for LBW and to 136subjects for IPC.

The analysis was conducted in three stages; uni-variate analyses, stepwise discriminant analysis, andhierarchical discriminant analysis. First, univariatet-test or chi square analyses determined all indepen-dent variables significantly associated with each ofthe two dependent variables. Next, a stepwise dis-criminant analysis determined the independent con-tribution each of these independent variables made tothe prediction of the two adverse outcomes, and theeffects of combining predictors to yield the best

discrimination between groups. Finally, a hier-archical discriminant analysis estimated the indepen-dent contribution psychosocial variables made to theprediction of adverse outcomes by forcing a block ofdemographic and biomedical variables into the equa-tion in a hierarchical fashion, and then assessing theincrease in the percent of cases correctly classified byentering psychosocial predictors in the second block.

Independent variables were categorized as demo-graphic, biomedical or psychosocial (see Table 1).Those associated with either dependent variable atP < 0.10 are discussed in the following paragraphs.Demographic variables include: (1) maternal age,dichotomized into 13-34 and 3541 years; (2) em-ployment status, dichotomized into women employedfull-time or on maternity leave versus those employedpart-time, unemployed, or working as homemakers.

RESULTS

Biomedical variables include: (1) maternal height;(2) Quetelet’s Index (weight/height squared) at 12-l 6

Table 1 displays a representative profile of the largenumber of demographic, biomedical and psycho-social independent variables tested for statisticalassociations with IPC and/or LBW. In general, de-mographic variables tend to be associated with IPC,and biomedical and psychosocial variables withLBW. Only one factor, family functioning, a psycho-social variable, was significantly associated with bothoutcomes.

weeks gestation [17]; (3) cigarette smoking; and (4)maternal history of a previous preterm birth.

Psychosocial variables include: (1) stressful lifeevents which occurred during pregnancy [181; (2)maternal worries about adjusting to life with a newbaby, a type of anticipated stress (Cronbach’s alphareliability = 0.83); (3) two scales of interpersonalsupport: emotional (alpha = 0.69) and instrumental(alpha = 0.64) which use items identified as importantin this population by Stack [19]; (4) maternal depres-sion, using the depression subscale of the BriefSymptom Inventory (alpha = 0.85) [20]; (5) familysize, defined as the number of persons named by therespondent as perceived family members [21]; (6)family functioning, defined as the woman’s percep-tion of her family’s instrumental and emotional ac-tivities and her satisfaction with her family’s per-formance. Family functioning was determined byscores obtained on three scales: (1) Smilkstein’s

five-item Family APGAR [22], using a five point scale(Cronbach’s alpha reliability = 0.90), with scoresgreater than 16 empirically defined as functional and16 or less as dysfunctional; (2) a truncated version ofOlson’s FACES II [23] with five-item Adaptability(alpha = 0.59) and Cohension Subscales (alpha =0.76); and (3) a shortened 16-item version ofHudson’s Index of Family Relationships [24](alpha = 0.95). A standardized composite z scoreconsisting of these subscales, was computed for eachwoman (alpha = 0.86). This composite score wasdichotomized into functional and dysfunctional cat-egories using an empirically derived cut-off point of-0.85. (Copies of all instruments are available fromthe authors on request.)

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Predicting low birthweight in black women 1323

Table 1. Probabilities of tests of association between predictor variables and adversepregnancy outcomes

Adverse outcomes

Study variables

Demographic

Maternal age c 19Maternal age > 35 yearsEmployment status-employedSocioeconomic statusMaternal educationHousehold incomeMarital statusHousehold size

Intrapartum Lowcomplications birtbweigbt

- -0.006 -0.03 -

--

-

BiomedicalMaternal heightQuetelet’s IndexCigarette smokingPrimaparityPrevious preterm birthPast history:

-Hypertension

-Urinary tract infection-Pre-eclampsiaPrenatal risk scores:

-Initial (at registration)-Developing problems (prenatal)

PsychosocialPrenatal stressful eventsMaternal worriesInterpersonal support:

-Emotional-Instrumental

Maternal depressionMaternal anxietyFamily sizeFamily functioning--(Family APGAR)Family functioning-(composite)

-Not significant at P = 0.10.

0.01- 0.02- 0.025- -- 0.09

--

-

0.010.01

0.020.010.01

0.0050.09 0.0010.006 0.005

Table 2 presents the results of a stepwise discrimin-ant analysis of those prenatal factors which, at sevenmonths’ gestation, predict intrapartum compli-cations. The variables are displayed by their order ofentry in the equation. All variables shown by uni-variate analyses to correlate significantly with intra-partum complications were eligible for entry into theanalysis.

The composite family functioning score was thesingle best predictor of complicated labor and de-livery; women in dysfunctional families are at higher

risk. Maternal age, with women 35 and older beingat higher risk, was second. Employment status, withworking women at higher risk, was third. Maternalheight was the fourth variable, with shorter womenbeing at higher risk. The absence of traditionalbiomedical risk factors such as prenatal infections orpre-eclampsia in this equation is notable.

Table 3 presents the sensitivity and specificityobtained using this four factor discriminant equation

of risk for intrapartum complications. This scale hasa sensitivity of 80%, and a specificity of 67%. In thissample of women, which has an 18% incidence ofhigh risk labors, 20 of the 57 women predicted tohave IPC actually experienced complicated labors,giving this scale a 35% positive predictive value anda 94% negative predictive value.

Table 4 presents the results of a stepwise discrimin-ant analysis of those prenatal factors which at sevenmonths’ gestation predict low birthweight. Familyfunctioning as determined by the Family APGAR

was the single best predictor of a subsequent lowbirthweight infant. Women with low family func-tioning scores were at higher risk. The reporting ofone or more stressful life events during the course ofpregnancy was the second predictor; Quetelet’s Indexat 12-16 weeks’ gestation was the third predictor, andcigarette smoking of more than one pack per day wasfourth. The profile of women with low family func-tioning, one or more stressful events, and lower

Table 2. Variables associated with intrapartum complications by stepwise discriminant analysis

Means/%

StepVariable entereh

Complicated Uncomplicated Fno. (n = 25) (n = 111) to enter P

I. Family function:composite score (% < -0.85) 60% 31% 1.91 0.006

2. Age: years (% > 35) 16% 3% 6.51 0.0253. Employment status (% working) 44% 22% 5.30 0.0254. Maternal height (mean in.) 62.8 in. 64 in. 3.94 0.05

For four-variable equation: F = 6.25, df = 4 131, P < 0.001

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3 2 4 KENNETH G REEB t?t al.

Table 3. Discriminant function prediction of women with andwithout intrapartum complications (IPC)

AClU0lintrapartumstatus

Predicted IPC status

Complicated Uncomplicated Total

Complicated

(&, (2i%,

25

(100%)Uncomplicated

0, (&%,Ill

(100%)Total 57 79 136

x2 = 18.25, df= I. P < 0.001. Sensitivity = 80%; specificity = 67%.

Quetelet’s indices, who are smokers, was at highestrisk.

The sensitivity and specificity obtained through theuse of this four factor scale to predict low birthweightare presented in Table 5. A 65% sensitivity and 84%specificity are obtained. With a 15% incidence of lowbirthweight in the 130 subjects included in the anal-

ysis, a 42% positive predictive value and a 93%negative predictive value are obtained.Table 6 displays the results of a hierarchical dis-

criminant analysis and a univariate prediction modelwhich both address the extent to which prenatalfamily functioning contributes to the prediction ofsubsequent IPC. Family functioning adds 20% to the60% sensitivity obtained using the block of demo-graphic and biomedical variables which were inclu-ded in the previous stepwise discriminant equation.This improvement in sensitivity is highly statisticallysignificant (P < 0.005). Family functioning reducesthe 70% specificity of the previous three variables by3%, yielding 67% specificity. The 39% positive pre-dictive value is also reduced slightly (-4%). Familyfunctioning (composite score) used alone as a predic-tor of IPC yields a 60% sensitivity, a 70% specificityand a 3 1% positive predictive value-quite compara-ble to that obtained by the combination of maternalheight, age and employment status.

Table 7 presents the contribution made to thesensitivity and specificity of the prediction of LBWmade by psychosocial variables, independent of themajor biomedical predictors. Family APGAR andstress together add 10% to the 55% sensitivity whichwas obtained using the combination of Quetelet’sIndex and maternal smoking. These two psychosocial

factors also add 11% to the specificity and 15% to thepositive predictive value of this equation. The FamilyAPGAR, alone outperforms the two strongest bio-medical predictors, yielding 65 % sensitivitycompared to the latter’s 55%, and 74% specificitycompared to 73% and 3 1% positive predictive value,

Table 5. Discriminant function classification of women into lowbirthweizht and nomml catezories

Actual birthWeight status

Low birthweight

N0lXlal

Total

Predicted birthweight status

LBW N0lXlal Total

(Z,, ,,:,, (HZ%,

(Z%, (K%)110

(100%)31 99 130

x2 = 22.04, df I. P < 0.001. Sensitivity = 65%; specificity = 84%.

compared to 27% for the combination of Quetelet’sIndex and smoking.

DISCUSSION

This study distinguishes between two types ofadverse pregnancy outcomes-intrapartum compli-

cations and low birthweight infants-and presentsevidence for different risk profiles for each outcomein a sample of urban black women. Existing riskscoring systems either address high risk pregnanciesin general or focus on one condition such as pre-maturity or intrauterine growth retardation. Theformer, nonspecific, approach decreases a scoringsystem’s predictive value. Risks of intrapartum com-plications have received little attention. Those prob-lems which occur during labor and delivery have beenincluded as risk factors in scoring systems for sub-sequent pediatric problems such as mental retard-ation, as opposed to using them as dependent vari-ables [151. Earlier identification of women-at-risk forproblems of labor and delivery in regionalized peri-natal care systems could reduce resultant maternaland infant morbidity and mortality by enabling re-ferral of these women to tertiary care obstetricsfacilities.

The risk profiles developed for both outcomes inthis study are generated from the broad profile ofbiopsychosocial factors summarized in Table 1. Someof the factors included in these risk profiles areknown to be predictors of adverse pregnancy out-comes. Advanced maternal age [9,26,28], maternalshort stature [26,27], low maternal weight in earlypregnancy [26,27], and cigarette smoking [26,27] are

included in other scoring systems. Working duringpregnancy is a controversial risk factor. However, ithas been associated with prematurity in France,where an intervention program includes releasingpregnant women from heavy work duties [29]. Therole of stress is receiving increasing attention, but it

Table 4. Stepwise discriminant analysis of low birthweight (LBW) status

MIXIS/%

step LBW Normalno. Variable entered (n = 20) (n = 110) to eFnter P

I. Family APGAR (% Q 16) 65% 27% 12.48 0.001

2. Stressful life events(% having one or more) 85% 55% 9.44 0.001

3. Quetelet’s Index(mean at 12-16 week) 22. I 25.5 7.40 0.001

4. Smoking(% smoking > one pack/day) 40% 16% 5.10 0.02

For four-variable equation: F = 9.34, df = 4, 125, P < 0.001.

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Predicting low birthweight in black women : 1325

Table 6. Sensitivity/specificity of intrapartum status: hierarchical and univariateprediction models (n = 136)

Variables

Block 1:HeightAge

SensitivityW)

SpecificityW)

Positivepredictive

valueW)

Employment 60 70 39Block :

Family functioning*(composite) 20 -3 -4

Total 80 67 35Family functioning

(composite), alone 60 70 31

= 7.92, 1, 131 d/ P < 0.005.

Table 7. Sensitivity/specificity of low birthweight status: hierarchical and uni-variate prediction models (n = 130)

Positive

predictiveSensitivity Specificity valueVariables W) W) W)

Block 1:Quetelet’s IndexSmoking 55 73 27

Block 2:Family APGAR*Stresst 10 II 15

Total 65 84 42Family APGAR, alone 65 74 31

= 7.92, 1, 126 df P

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1326 KENNETH G. REEB et al.

preventive interventions would be more likely to beeffective.

This study focuses on pregnant urban blackwomen, a population at empirically high risk forprematurity and for infant mortality in the UnitedStates [3]. These rates are particularly high in manycities in the Northeastern U.S. In 1982, the city ofCleveland reported infant mortality rates of16.3/1000 for white infants and 29.7/1000 for blacks[25]. To date, risk scoring systems have not focusedon particular sub-population groups. An ethnic orracial group-specific risk system is likely to improveprecision. In addition, psychosocial instrument scoresmay differ between ethnic groups. For example, acolleague found significant differences between meanscores of these family functioning instruments forlow-income blacks compared to middle-incomeblacks and to low- and middle-income whites(M. Schein, personal communication, 1985).

Although previous studies have shown associationsbetween other psychosocial variables and adversepregnancy outcomes [1 l-1 3.31.321. it should beemphasized that the factors identified here are basedon current state-of-the-art questionnaires and thatthey are strong predictors of these outcomes in lowincome urban black women.

Development of a broadly-based, biopsycho-socially-oriented prenatal risk scoring system appearsto be feasible, judging by the strength of thesereported associations. Each of these factors can beconverted to a practical, clinical questionnaire. Thus,they can be incorporated in any one of severalexisting RSS to produce a screening device tailoredfor use in urban black pregnant women. An effectivescreening program can then be coupled with a pre-ventive intervention program focused on women athighest risk. This can improve the program’s practi-cality and its likelihood of effectiveness in reducingthe distressingly high prematurity and infant mor-tality rates among U.S. urban black women. A logicaloutgrowth of our findings will be a family-oriented,support-building, stress-lessening intervention de-signed to reduce adverse pregnancy outcomes. Oldser al. [33] have reported a similar trial in rural,predominantly white women.

One key to improving the maternal-fetal health of

black women in the United States may be to broadent:ic xope of prenatal assessment and maternal healthservice.; to include a psychosocial and family focus.Decreasing adverse pregnancy outcomes in urbanbiack women may well become a family matter.

Acknowledgements-We are indebted to Sandra McGee,Patricia Ross and Julie Ziegler for technical assistance andto Jean Szucs for preparation of the manuscript. Thisresearch was supported in part by the Robert Wood John-son Foundation’s Research and Development Program toImprove Patient Functional Status (grant no. 7144) and wasaided by Social and Behavioral Science Research grant no.12-156 from the March of Dimes Birth Defects Foundation.

REFERENCES 23

1 Backett E. M., Davies A. M. and Petros-Barvazian A.The risk approach in health care: with special reference

4.

5.

6.

7.

8.

9.

IO.

I I.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21

22

to maternal and child health, including family planning.Public Health Papers No. 76. World HealthOrganization, Geneva, 1984.

2. Behrman R. Preventing low birth weight: a pediatricperspective. J. Pediat. 107 842 1985.

3. McCormick M. C. The contribution of low birth weightto infant mortality and childhood morbidity. New Engl.J. Med. 312. 82 1985.World Health Organization. Genera Wkt.v Epidem. Rec.59 205 1984.Casson R. I. and Sennett E. S. Prenatal risk assessmentand obstetric care in a small rural hospital: comparisonwith guidelines. Can. med. Ass. J. 130, 1311, 1984.Sylvain J. P. E. Editorial: regionalization of perinatalservices in the use of high-risk scoring forms. Can. med.Ass. J. 130, 1269 1984.Committee to Study the Prevention of Low Birthweight,Division of Health Promotion and Disease Prevention.Institute of Medicine, National Academy of Sciences.Preventing Low Birth Weight p. 75. National AcademyPress, Washington, D.C., 1985.

Guzick D. S.. Daikoku N. H. and Kaltreider D. F.Predictability of pregnancy outcome in pretermdelivery. Obstet. Gynec. 63 645 1984.Goodwin J. W., Dunne J. T. and Thomas B. W.Antepartum identification of the fetus at risk. Can. med.Ass. J. 101, 58. 1969.Gravett M. G. Causes of preterm delivery. Perinatotogy8 246 1984.Oakely A. Social support in pregnancy: the “soft” wayto increase birthweight. Sot. Sci. Med. 21, 1259, 1985.Norbeck J. S. and Tilden V. P. Life stress, social supportand emotipnal disequilibrium in complications of preg-nancy: a prospective multivariate study. J. Hlth sot.Behaa. 24 30 1983.Nuckolls K. B., Cassel J. and Kaplan J. H. Psychosocial

assets, life crises and the prognosis of pregnancy. Am.J. Epidem. 95 431 1972.Main D. M., Gabbe S. G., Richardson D. and StrongS. Can preterm deliveries be prevented? Am. J. Obstet.Gynec. 151, 892, 1985.Hobel C. J., Hyvarinen M. A., Okada 0. M. and Oh W.Prenatal and intrapartum high-risk screening. I. Predic-tion of the high risk neonate. Am. J. Obstet. Gynec. 117,I, 1973.Hollingshead A. B. Two Factor Index of Social ClassMeasurement. Privately Published, New Haven, Conn.1957.Metropolitan Life Insurance Company-StatisticalBulletin. Jan-Mar, pp. 2&23, 1984.Dohrenwend B. S.,-Krasnoff L., Askenasy A. R. and

Dohrenwend B. P. Exemplification of a method forscaling life events: the Peri Life Events Scale. J. Hlth sot.BehaLl: 19 205 1978.Stack C. B. AN Our Kin: Strategies For Survival In ABlack Community. Harper & Row. New York, 1974.Derogatis L. R.. XL-90: Administration Scoring andProcedures Manual-i. The revised version and otherinstruments of the Psychopathology Rating Scale series.Johns Hopkins University School of Medicine,Baltimore, Md, 1977.Reeb K. G., Graham A. V., Kitson G. C., ZyzanskiS. J.. Weber M. A. and Engel A. Defining family infamily medicine: perceived family vs household struc-ture in an urban black population. J. Fam. Pratt. 23_351, 1986.

Smilkstein G. The family APGAR: a proposal for afamily function test and its use by physicians. J. Fam.Pratt. 6 1231 1978.Olson D. H., Sprenkle D. H. and Russel C. S. Circum-plex model of marital and family systems: cohesion andadaptability dimensions, family types and clinical appli-cations. Fam. Proc. 18 3. 1979.

8/18/2019 1987 Reeb

7/7

Predicting low birthweight in black women 1327

24. Hudson W. W. The Clinical Measurement Package: Prevention of preterm births: a perinatal study inA Field Manual. Dorsey Press, Homewood, Ill., 1982.

2;. Citv of Cleveland Population, Birth and Death Statis-Haguenau, France. Pediatrics 76 154 1985.

30. Hobel C. J. Prevention of preterm delivery. In Feraltics: 1982. Bureau of kital Statistics, City of ClevelandHealth Department.

26. Creasv R. K.. Gummer B. A. and Liggins G. C. Systemfor predicting spontaneous prete& birth. Obstet.Gynec. 55 692 1980.

27. Fedrick J. and Anderson A. B. M. Factors associatedwith spontaneous preterm birth. Br. J. Obstet. Gynaec.83 342 1976.

28. Nesbitt R. E. L. Jr and Aubry R. H. High riskobstetrics: value of semiobjective grading system inidentifving the vulnerable group. Am. J. Ubstet. Gynec.

Physiology and Medicine: The Basis of Perinatology(Edited by Beard R. W. and Nathanielsz P. E.). MarcelDekker, New York, 1984.

31. Smilkstein G., Helsper-Lucas A., Ashworth C., Mon-tano D. and Page1 M. Prediction of pregnancy compli-cations: an application of the biopsychosocial model.Sot. Sci. Med. 18 315 1984.

32. Ramsey C. N. Jr, Abel1 T. D. and Baker L. C. Therelationship between family functioning, life events,family structure and the outcome of pregnancy. J. Fam.Pratt. 22 521 1986.

103, 972,-l969._

33. Olds D. L., Henderson C. R. Jr, Tatelbaum R. and29. Papiernik E. J., Bouyer J., Druyfus J., Collin D., Chamberlin R. Improving the delivery of prenatal care

Winisdorffer G., Guegen S., Lecomte M. and Lazar P. and outcomes of pregnancy: a randomized trial of nursehome visitation. Pediatrics 77 16 1986.