Neighborhood Risk Factorsfor Low Birthweight in Baltimore:A Multilevel Analysis

Patricia O'Campo, PhD, Xiaonan Xue, PhD, Mei-Cheng Wang, PhD, andMargaret O'Brien Caughy, ScD

IntroductionLow birthweight remains a signifi-

cant public health problem in the UnitedStates. Low birthweight (defined as aweight of less than 2500 g at birth for alive-bom infant) has declined very littleover the past several decades and is evenon the rise among some high-risk groups. 'Much research has focused on individual-level risk factors for low birthweight;individual-level models, however, havebeen able to explain only a small propor-tion of the overall variability seen forbirthweight.2 Moreover, it is increasinglybeing recognized that environmental fac-tors contribute to the risk of low birth-weight.3-7

Previous studies of low birthweighthave been limited to individual factors intheir conceptualization of social risk.89Social risk, however, should also includeenvironmental stressors, which shape indi-vidual vulnerability and resistance to riskfactors for health.40-20

Analyses that include both individual-level and macrolevel data-referred to ascontextual or multilevel models-haveseveral advantages.21 First, multilevelanalytic methods are more consistent withsocial theories than are traditional meth-ods of analysis (e.g., ordinary regression)in that they explicitly accommodate themultiple levels of data.22 Second, multi-level methods can contribute new knowl-edge to our current understanding ofpublic health issues by allowing for theinclusion of macrolevel factors in ourcurrent explanatory models, thereby bridg-ing the micro-macro gap by increasingour understanding of how contextualfactors translate into differences in indi-vidual-level risk.23-25 Further, these meth-ods may eliminate potential confoundingof individual-level explanatory models

due to the omission of macrolevel fac-tors.23'26 Finally, by improving our under-standing of how contextual factors influ-ence individual health outcomes, we willbe better equipped to design effectiveintervention strategies.'9'20

The analysis presented here is part ofa larger study looking at indicators ofneighborhood and social risk for poorpregnancy outcome in Baltimore, Md. Wewere specifically interested in answeringthe following questions about multilevelanalyses and the study of risk factors forlow birthweight: (1) Are neighborhood-level variables directly related to anincrease or decrease in risk of lowbirthweight? (2) Do individual-level riskfactors for low birthweight behave differ-ently depending on the characteristics ofthe neighborhood in which a womanresides? (3) What are the interventiondesign or policy implications of includingmacrolevel variables in research on lowbirthweight?

MethodsData

Individual-level variables. Comput-erized birth certificates with nonmissinginformation on birthweight and matemalcharacteristics were obtained for the

Patricia O'Campo and Margaret O'Brien Caughyare with the Department of Maternal and ChildHealth, and Mei-Cheng Wang is with theDepartment of Biostatistics, Johns HopkinsSchool of Hygiene and Public Health, Baltimore,Md. Xiaonan Xue is with the Department ofBiometry and Statistics, School of Public Health,New York State University, Albany.

Requests for reprints should be sent toPatricia O'Campo, PhD, Department of Maternaland Child Health, 624 N Broadway, Room 189,Baltimore, MD 21205.

This paper was accepted September 16,1996.

American Journal of Public Health 1113

O'Campo et al.

calendar years 1985 through 1989 fromthe city health department's Bureau ofBiostatistics (n = 50 757). The healthdepartment routinely codes data withcensus tract identifiers. A program such as

Map Info for Windows (200 Broadway,Troy, NY 12180) can then be used to linkthese geocoded data with data that de-scribe census tract-level characteristics.27

We had wanted to examine howmacrolevel social factors contributed toracial differences in low birthweight.However, we were not able to get census

tract-specific risk of low birthweight byrace for the majority of the census tracts.Although births in Baltimore during thestudy period were predominantly (73%)to African-American women, very fewcensus tracts had enough Black and Whitebirths to make reliable estimates; themajority of Baltimore census tracts haveeither predominantly White or predomi-nantly Black births, making estimationdifficult.

Census tract-level variables. Dataon census tract characteristics were ob-tained from several sources. Decisionsabout which variables to use to character-ize the census tracts were based on severalconsiderations. First, census tract-levelvariables had to be readily available froma routinely collected data source. Readyavailability of these data will facilitate use

of the analytic techniques reported here infuture research and intervention planning.Second, census tract-level variables were

chosen to represent a wide spectrum ofcharacteristics of neighborhoods, includ-

ing socioeconomic indicators and physi-cal and social characteristics.

The Community Planning Divisionof the City of Baltimore's Department ofPlanning makes available data on a

variety of neighborhood characteristics atthe census tract level, including homeownership, numbers of housing violationsissued, per capita crime rates, and num-

bers of active neighborhood communitygroups (which we used as a measure ofcommunity empowerment). We obtainedunemployment statistics for BaltimoreCity for 1988 and 1989 from the Mary-land Department of Employment andEconomic Development.

There are sources of commerciallyavailable data that yield profiles of censustracts. Per capita income and informationon household wealth (e.g., equity inhomes, vehicles, assets, mortgages, andother debts and real estate ownership) atthe census tract and block group level can

be obtained through Claritas/NPDC (POBox 610, Ithaca, NY 14851-0610). Thisinformation is updated annually on thebasis of census data, marketing surveys,and projections.

We wanted more than one indicatorof social class, as previous researchershave called for the use of more than one

indicator to capture the complexity of thisrisk factor.28 Per capita income andhousehold wealth were thought to benonlinearly related to low birthweight. Percapita income was therefore categorizedinto quartiles, based on the distribution ofthis variable for the city as a whole, and

dummy variables were created. Becauseof non-normality, a log transformation ofthe wealth was used to more closelyapproximate a normal distribution.

The study variables are summarizedin Table 1.

Analytic Method

Models that include two or more

levels of information should be analyzedby means of appropriate statistical meth-ods that accommodate the different levelsof data.'52932 The analytic method we

used is based on the two-stage regressionmethod proposed by Wong and Mason3'and Bryk and Raudenbush23 for theanalysis of multilevel models. We choseto use a two-stage regression methodbecause it gives consistent estimators ofrisk factors when sample sizes are as largeas our data set, which has hundreds ofobservations per census tract or census

tract cluster. In performing two-stageregression, we wanted to obtain informa-tion on two types of relationships: directeffects of individual-level and macrolevelfactors on low birthweight and interactioneffects between micro- and macrolevelfactors and low birthweight.

The first stage of the analysis in-volved the generation of 180 census

tract-specific regression models on indi-vidual-level data. (There are 198 residen-tial census tracts in Baltimore City.However, because of low frequencies oflow birthweight for 18 of the census

tracts, individual-level regressions yieldedunstable parameter estimates. Therefore,data from these 18 census tracts were

combined with those of adjacent census

tracts with similar sociodemographic infor-mation, which improved estimation andallowed for data on all births in the city tobe used.) In these models, low birthweightwas regressed on the four individual-levelvariables: matemal age, matemal educa-tion, prenatal care, and health insurancestatus. We had approximately 200 to 900observations per census tract. We refer tothe first stage of the analysis as themicrolevel analysis. Although the mac-

rolevel attributes are ignored at this stage,context is accounted for because a regres-sion equation is generated for each census

tract separately. For the second stage ofthe two-stage regression analysis, themacrolevel analysis, we consider only the180 census tracts. The distributions of thefive estimates obtained in the first stage(the intercept and the regression coeffi-cients for the four individual-level vari-ables) were each regressed on the census

tract-level variables separately. Thus, at

11 14 American Journal of Public Health

TABLE 1-Variables Used in Multilevel Analysis of Risk for Low Birthweight,TABLE 1 -Variables Used in Multilevel Analysis of Risk for Low Birthweight,

Baltimore, Md, 1985 through 1989

Variable Type Distribution, Mean (SD)

Individual-levelLow birthweighta Binary .12 (.32)Maternal age, y Continuous 24.2 (5.7)Maternal education, y Continuous 12.0 (3.5)Trimester of prenatal care initiationb Categorical Median is 2Health insurance statusc Binary .27 (.44)

Census tract-levelRatio of home owners to renters Continuous 1.4 (1.3)No. of community groups Continuous 3.0 (2.7)Unemployment rate, % Continuous 7 (4.3)Rate of housing violations, % Continuous 9 (7)Per capita crime rate, % Continuous 11 (18)Average wealth, $000 Continuous log 99 (53)Per capita income, $000 Dummy 12 (7)

a<2500 g = 1, -2500 g = 0.bFor women who received no prenatal care, the value of this variable was 4. Therefore, highernumbers represent less adequate prenatal care.

cMedicaid = 1, non-Medicaid = 0.

July 1997, Vol. 87, No. 7

Low Birthweight Analysis

this stage the attributes of the contexts are

brought into the model. Also at this stage,we can use model-building techniqueswhereby "best fit" models can be obtain-ed. Multicollinearity was not a problem inour analysis; the range of correlationsbetween neighborhood-level variables wasfrom .009 to .625, with only 2 of 40correlation pairs at the .60 or higher leveland 30 of the 40 pairs at .30 or lower. Allcontinuous variables were centered at themean.23 Therefore, the reference group foreach continuous variable is those individu-als who had an average value for thevariable in question or census tracts at an

average level of that variable. A detailedaccount of the methods and rationalebehind the statistical technique used herecan be obtained from the authors.

ResultsThe final multilevel model is shown

in Table 2. Although nonsignificant vari-ables were retained for adjustment pur-

poses (see Table 2 footnotes), only statisti-cally significant predictors are shown inthe table.

Direct Effects ofIndividualand Census Tract Variables

The estimated odds ratios and theircorresponding confidence intervals for thedirect effects of the individual-level andcensus tract-level variables are displayedin Table 2. Because all continuous vari-ables were centered at the mean, thereference groups for the odds are thoseindividuals with an average value for thecontinuous variable. All individual-levelvariables were significantly related to therisk of low birthweight and therefore wereretained in the final model. Higher mater-nal education had a protective effect forlow birthweight. Prenatal care was signifi-cantly related to the risk of low birth-weight. Later initiation of prenatal care

was related to an increased risk of lowbirthweight. Mothers enrolled in Medic-aid were at a higher risk for lowbirthweight than other women. Finally,matemal age was related to a slightincrease in risk of low birthweight, a

finding that is consistent with some

previous research but not all.6'9'33'34 Forexample, some previous studies haveshown that for African-Americans, per-haps because of high-risk environments,risk of low birthweight increases withmatemal age. 6'9'33 Our sample consistedpredominantly of African Americans andcontained a high proportion of low-

income women. We would have liked toinvestigate interactions between age andrace/ethnicity but could not with our data.

Of the census tract-level variables,per capita income had a significant directrelationship to risk of low birthweight.Residents of census tracts with a per

capita income of less than $8000 had a

significantly higher risk of low birth-weight than did women in census tractswith a per capita income of more than$8000.

Interaction Effects of CensusTract Variables

Our second study question was

whether individual-level risk factors forlow birthweight behave differently depend-ing on the neighborhood in which a

woman resides. The results indicate thatthe census tract-level variables did indeedmodify the associations between theindividual-level factors and the risk of lowbirthweight. The coefficients and corre-

sponding standard errors of these interac-tion effects of the macrolevel variables are

displayed in Table 2. A few examples willbe used to demonstrate how these coeffi-cients are interpreted.

Medicaid health insurance status.Per capita income modified the relation-ship between health insurance status andlow birthweight. Having Medicaid as

health insurance increased the risk for lowbirthweight (odds ratio [OR] = 1.49). TheMedicaid health insurance odds ratio forlow birthweight is smaller for women

living in neighborhoods with a per capitaincome of less than $8000 than forwomen living in neighborhoods with a per

capita income of $11 000 or more. TheMedicaid health insurance odds ratio isdiminished by an amount equal to the ,Bfor the interaction effect of per capitaincome less than $8000 (-.439). Thelow-birthweight odds ratio decreases to1.07 (e .103+ .401 - 439) for Medicaid as

health insurance when a woman lives in a

neighborhood with a per capita income ofless than $8000. Similarly, the negative ,Bfor per capita income of $8001 to $11 000also indicates a diminished odds ratio; forwomen living in those neighborhoodscompared with women living in neighbor-hoods with a per capita income of more

than $11 000, the low-birthweight oddsratio is 1.10 (e 401 - .302)

Prenatal care initiation. Regardingtrimester of prenatal care initiation andrisk of low birthweight, the log odds oflow birthweight increases by .227 forevery trimester after the first that prenatalcare is initiated. However, this associationis modified by the unemployment rate andthe level of wealth in a woman's neighbor-hood. For every percentage point increase

American Journal of Public Health 1115

TABLE 2-Parameter Estimates from the Final Two-Level Logistic Model forRisk of Low Birthweight, Baltimore, Md, 1985 through 1989

Odds 95% Confidence1 SE(g) Ratio Interval

Direct effectsMaternal age .022 .004 1.02 1.01, 1.03Maternal education -.129 .018 .87 .85, .91Prenatal care .227 .025 1.25 1.19,1.32Health insurance status .401 .068 1.49 1.31, 1.70Per capita income < $8000 .103 .049 1.11 1.02,1.22

Interaction effects via maternal ageaUnemployment .002 .001 ...

Interaction effects via maternal educationPer capita crime rate -.769 .329 ... ...

Interaction effects via prenatal careblog(average wealth) -.118 .054 ... ...

Unemployment -.017 .007 ...

Interaction effects via health insurancestatus

Per capita income < $8000 -.439 .090 ... ...

Per capita income $8000-$11 000 -.302 .092 ... ...

Note. Parameter estimates are for individual-level, neighborhood-level, and interactioneffects.

aThe interaction effects via maternal age are adjusted for number of community groups.bThe interaction effects via prenatal care are adjusted for crime rate and rate of housing

violations.

July 1997, Vol. 87, No. 7

O'Campo et al.

in the unemployment rate over the cityaverage, the log odds of low birthweightassociated with trimester of prenatal care

initiation decreases by .017 (see the betafor the interaction effect ofunemploymentfor prenatal care, -.017, in Table 2). That

is, as unemployment increases, the protec-tive effect of early prenatal care initiationdiminishes. Therefore, for neighborhoodswith an unemployment rate of 9%, whichis 2 percentage points above the cityaverage, the log odds of low birthweight

associated with later prenatal care initia-tion is 0.193 (e2(-.017) + .227) (OR = 1.21).(Because unemployment is centered at thecity mean of 7%, we multiply theunemployment beta by 2 rather than 9 toobtain the unemployment effect of 9%.)In contrast, for neighborhoods with anunemployment rate of 5%, or 2 percent-age points below the city average, the logodds of low birthweight associated withlater prenatal care initiation is 0.261(e-2(-.017) + 227) (OR = 1.30). These num-bers suggest that in low-risk neighbor-hoods, as measured by low unemploy-ment, the protective effect of earlyinitiation of prenatal care is stronger thanit is in higher-risk neighborhoods. Theassociation between prenatal care initia-tion and risk of low birthweight for threeneighborhoods with different unemploy-ment rates is displayed in Figure 1.Trimester of prenatal care initiation is notas strongly related to the risk of lowbirthweight in neighborhoods that have ahigher unemployment rate. A summary ofthe direct and interaction relationships ofthe macro- and individual-level risk fac-tors for low birthweight is presented inFigure 2.

DiscussionPublic health researchers and profes-

sionals should be as much concemed withsocial risk factors as they are withindividual-level risk factors. Until moreresearch is dedicated to the study ofindividuals within the contexts in whichthey live and work, the relative impor-tance of targeting individual or contextualfactors to improve health will remainunknown. Past studies have suggestedthat larger social factors may be moreimportant than individual-level risk fac-tors in producing adverse health out-comes.35

Our findings indicate that includingmacrolevel social factors in studies of lowbirthweight is useful for obtaining betterexplanatory models. We found that indica-tors of social stratification, particularly percapita income, were directly related to therisk of low birthweight in Baltimore. Thisfinding suggests that future efforts aimedat reducing low birthweight might bemore effective if efforts are made to targetmacrolevel social factors rather than, or inaddition to, the usual individual-levelfactors (e.g., health behaviors, early initia-tion of prenatal care).

Our indicator of community empow-erment, the number of community groupsper census tract, was not associated with

July 1997, Vol. 87, No. 7116 American Journal of Public Health

2A2

19

1.8 ~~~~~~~~~~~1.77 It'rCWhIS grSow for1.8-. v.......... ............. ........... f hM*_W1W tP

.s 1isfiratbnPtor-

*OddsrdtedaeIA . 3. . . .....~~~~~~~~~~~~~~~~~~~ofwsttfetCitI2d 3rd No care 2nd 3rd No Cae ' 2nd 3rd NoCare

i

Trima ofPrmaA_ Cam llidiWonHi4j Avg L

Neighborhood Unemployment Levels

FIGURE 1-Odds ratios for risk of low birthweight, Baltimore, Md, 1985through 1989, by trimester of prenatal care initiation withinneighborhoods with high, average, and low levels ofunemployment, illustrating the interaction betweenunemployment levels and timing of prenatal care initiation.

NkWhhIxhQ9d-_tve Individual-level

Per CapitaCrime Low Maternal

Education r K.Unemployment Ma+)a

Average Wealth (.) LBW

Per Capita Income<$8k

HealthPer Capita Income - Inurance Status$8-1 lk

Note. Heavy arrows represent direct effects on low birthweight; odds ratios (ORs) for thesedirect effects are shown in parentheses. Interaction effects are indicated by broken-linearrows. These interaction effects modify the relationship between individual-level variablesand low birthweight. For example, the increased risk of low birthweight for women with lowlevels of education (OR = 1.15 in neighborhoods with average crime rates) is stronger (+)in high-crime than in low-crime neighborhoods; the increase in risk with increasing maternalage (OR = 1.02 in neighborhoods with average unemployment levels) is stronger (+) inhigh-unemployment than in low-unemployment neighborhoods.

FIGURE 2-Risk of low birthweight (LBW) in Baltimore, Md, 1985 through1989: direct and interaction effects of neighborhood-level andindividual-level risk factors.

Low Birthweight Analysis

low birthweight. While the number ofactive community groups is a crudeindicator of community empowerment,we had expected this factor to have aprotective effect against low birthweight.

Our analyses indicate that there issubstantial interaction between mac-rolevel factors and individual-level riskfactors for low birthweight. Indicators ofsocial class, and environmental stressorssuch as poor housing conditions and highcrime and unemployment rates, werefound to modify the relationship betweenindividual-level risk factors and lowbirthweight. This finding has implicationsfor the design of policies and interven-tions for improving pregnancy outcomes.For example, previous policy and interven-tion recommendations were based primar-ily on individual-level analyses alone.36'37Such analyses may overestimate theimportance of changing individual-levelrisks as a strategy to reduce low birth-weight. Our analyses support this idea.For example, although our findings agreewith previous studies that show that earlyinitiation of prenatal care reduces the riskof having a low-birthweight infant, ourfindings indicate that this protective effectvaries by residential context. In high-riskneighborhoods, as indicated by averagewealth or unemployment rates, the protec-tive effect of prenatal care was dimin-ished. In lower-risk neighborhoods, thebenefits of early initiation of prenatal carewere more substantial. Thus, when thedesign of policies or interventions is basedonly upon individual-level analyses, thebenefits of interventions aimed at increas-ing earlier initiation of prenatal care,which are usually targeted toward high-risk populations, may be overestimated.

Although with most of our interac-tion effects higher-risk environments di-minished the protective effects of indi-vidual attributes, not all macrolevel factorsbehaved consistently. For example, theprotective effect of early initiation ofprenatal care was greatest for womenliving in neighborhoods with high levelsof housing violations. This suggests thatthe mechanisms of macrolevel risk factorsfor low birthweight and possibly otheradverse health outcomes are complex.Consideration of a wide variety of environ-mental stressors and characteristics wouldbe important in future studies.

Future studies might include bothindividual- and neighborhood-level vari-ables of the same social factors (e.g.,mother's income and average income offamilies in a census tract). These variablesdo not necessarily measure the same

construct.2' For example, high levels ofpersistent neighborhood poverty or unem-ployment may indicate a lack of politicaland economic empowerment and re-sources that affects all persons living inthat neighborhood, independent of theirown poverty or unemployment status.

Our study used routinely availabledata as sources for our indicators of socialrisk. The rationale behind the use ofroutinely available data was to developuseful indicators that might easily beincorporated into future studies of mater-nal and child health. However, researchersshould not overlook the possibility ofdeveloping and using a more comprehen-sive set of social indicators. For example,to investigate community empowermentwe might have included other indicatorsthat are not necessarily routinely avail-able. Such indicators might include satis-faction with neighborhoods, social cohe-sion, voting patterns, presence of socialprograms, intervention programs target-ing pregnant women and new mothers(e.g., Healthy Start), and churches orchurch-based programs. A more compre-hensive set of indicators for housingconditions might include crowding,38'39proportion of abandoned housing, age ofdwellings, presence of lead paint, andother specific housing violations. Suchdata can be collected by a variety ofmethods, and several researchers havedeveloped systematic means of collectingsuch information on context.Y>2 Futuremultilevel studies of low birthweight andvery low birthweight might investigatethe ability of macrolevel social factors toexplain the low-birthweight gap betweendifferent racial and ethnic groups.' Al-though previous studies have attempted toexplain the gap by looking at individual-level social factors,8'9 recent studies ofsegregation43 and African-American politi-cal empowerment44 suggest that multi-level models may facilitate further expla-nation of the reasons for the gap. Finally,future studies should adjust for and lookfor interaction effects with a more compre-hensive list of individual-level risk fac-tors, such as presence of pregnancy-related or preexisting medical compli-cations, health behaviors (e.g., smokingduring pregnancy), and a better indicatorof adequacy of prenatal care. The availabil-ity or quality of data in Maryland's vitalrecords registration system precluded usfrom including such information in ouranalyses.

We have shown that multilevel mod-els should be used in future analyses ofmaternal and child health outcomes;

software is becoming accessible and dataon contexts are routinely available. Suchmodeling methods can not only facilitatethe development of better explanatorymodels but can form the basis of betterpolicy and intervention design. Z

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