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V O LU M E 4 9 N U M B E R 2 J U N E 2 0 0 8

FAMILY, SCHOOL AND NEIGHBORHOOD CONTEXTS OF HEALTHSeven Years Later: Effects of a Neighborhood Mobility Program onPoor Black and Latino Adults’ Well-BeingRebecca C. Fauth, Tama Leventhal, and Jeanne Brooks-Gunn

Racial/Ethnic Differences in Asthma Prevalence: The Role of Housingand Neighborhood EnvironmentsEmily Rosenbaum

Capital and Context: Using Social Capital at Home and School toPredict Child Social AdjustmentMikaela J. Dufur, Toby L. Parcel, and Benjamine A. McKune

LIFE COURSE TRANSITIONS, TRAJECTORIES & HEALTHGrowing Up Fast: Stress Exposure and Subjective ‘Weathering’ inEmerging AdulthoodHolly Foster, John Hagan, and Jeanne Brooks-Gunn

Health and the Educational Attainment of Adolescents: Evidence fromthe NLSY97Steven A. Haas and Nathan Edward Fosse

Black and White Chains of Risk for Hospitalization Over 20 YearsKenneth F. Ferraro and Tetyana Pylypiv Shippee

Workplace Support, Role Overload, and Job Satisfaction of Direct CareWorkers in Assisted LivingRita Jing-Ann Chou and Stephanie A. Robert

Major Life Events, Their Personal Meaning, Resolution, and MentalHealth SignificanceJohn R. Reynolds and R. Jay Turner

3357 JHSB 5/6/08 1:51 PM

EditorELIZA K. PAVALKO

Indiana University

Deputy EditorsPAMELA BRABOY JACKSON

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Associate Editors

American Sociological Association Executive Officer

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ANGELO A. ALONZOOhio State University

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RONALD J. ANGELUniversity of Texas

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CHRISTOPHER BROWNINGOhio State University

DEBORAH CARRRutgers University

RALPH CATALANOUniversity of California, Berkeley

RUTH C. CRONKITEVA Palo Alto Health Care System

MARY L. FENNELLBrown University

BRIAN K. FINCHSan Diego State University

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RICHARD ALLEN MIECHUniversity of Colorado Denver

SAMUEL NOHUniversity of Toronto

FRED C. PAMPELUniversity of Colorado at Boulder

JOHN R. REYNOLDSFlorida State University

CHRISTIAN RITTERNortheast Ohio Universities Colleges of Medicine and Pharmacy

STEPHANIE A. ROBERTUniversity of Wisconsin–Madison

SUSAN ROXBURGHKent State University

TERESA L. SCHEIDUniversity of North Carolina at Charlotte

MICHAEL J. SHANAHANUniversity of North Carolina at Chapel Hill

MARK TAUSIGUniversity of Akron

ERIC R. WRIGHTIndiana University/Purdue University

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3357 JHSB 5/6/08 1:51 PM

FAMILY, SCHOOL AND NEIGHBORHOOD CONTEXTS OF HEALTHSeven Years Later: Effects of a Neighborhood Mobility Program on Poor Black and Latino Adults’ Well-Being 119Rebecca C. Fauth, Tama Leventhal, and Jeanne Brooks-Gunn

Racial/Ethnic Differences in Asthma Prevalence: The Role of Housing and NeighborhoodEnvironments 131Emily Rosenbaum

Capital and Context: Using Social Capital at Home and School to Predict Child Social Adjustment 146Mikaela J. Dufur, Toby L. Parcel, and Benjamine A. McKune

LIFE COURSE TRANSITIONS, TRAJECTORIES & HEALTHGrowing Up Fast: Stress Exposure and Subjective ‘Weathering’ in Emerging Adulthood 162Holly Foster, John Hagan, and Jeanne Brooks-Gunn

Health and the Educational Attainment of Adolescents: Evidence from the NLSY97 178Steven A. Haas and Nathan Edward Fosse

Black and White Chains of Risk for Hospitalization Over 20 Years 193Kenneth F. Ferraro and Tetyana Pylypiv Shippee

Workplace Support, Role Overload, and Job Satisfaction of Direct Care Workers in Assisted Living 208Rita Jing-Ann Chou and Stephanie A. Robert

Major Life Events, Their Personal Meaning, Resolution, and Mental Health Significance 223John R. Reynolds and R. Jay Turner

Volume 49 Number 2 June 2008

Racial/Ethnic Differences inAsthma Prevalence: The Role of Housingand Neighborhood Environments*

EMILY ROSENBAUMFordham University

Journal of Health and Social Behavior 2008, Vol 49 (June): 131–145

This article examines the prevalence of asthma among New York City householdsfrom 10 racial/ethnic groups, and it explores whether differential exposure to po-tentially adverse housing and neighborhood conditions helps to mediate ob-served disparities. After adjusting for household size, Puerto Rican householdsexhibit the highest levels of asthma, followed by other Hispanic and black house-holds. Mexican, Chinese, and Asian Indian households exhibit the lowest levelsof asthma. Results from multilevel logistic regression models indicate that expo-sure to deteriorated housing conditions and perceptions of low social cohesionin the neighborhood significantly elevate the odds of asthma. Controlling forthese conditions along with household characteristics reduces the dispropor-tionately high levels of asthma among Puerto Rican and black households, al-though they remain significantly higher than the level among white households.

131

Asthma has been steadily increasing inprevalence since 1980 (Institute of Medicine2000; Rhodes et al. 2003), and its greaterprevalence among low-income and urban resi-dents has caused it to be termed “the other in-ner-city health crisis” (relative to HIV/AIDS;Boardman, Finch, and Hummer 2001). Theconcentration of higher-than-average asthmarates among poor, generally minority, inner-city residents, moreover, has been thought tobe linked to the low quality of housing suchgroups occupy, as well as the concentration ofoutdoor pollutants in their neighborhoods(Katz, Kling, and Liebman 2001). Yet, largelybecause of a lack of data sets combining healthindicators and housing quality at the individuallevel and aspects of conditions prevailing at theneighborhood level, there has been little work

done to date that explicitly evaluates the roleplayed by housing and neighborhood condi-tions in mediating observed disparities in asth-ma. This article helps to address this gap in theliterature, by estimating multilevel models pre-dicting asthma prevalence among New YorkCity households as a function of housing con-ditions and features of the surrounding area.

DIFFERENTIALS IN ANDCORRELATES OF ASTHMA

Rates of self-reported asthma vary acrossracial/ethnic groups. In 2001, among personsreporting a single race, non-Hispanic blackswere most likely to report having asthma (with8.5 percent of respondents reporting that theycurrently suffer from the condition), followedby non-Hispanic whites (at 7.2 percent), otherrace non-Hispanics, and finally Hispanics(both at 5.9 percent) (Rhodes et al. 2003).While the black/white differential has beenconsistently reported in other sources, includ-ing national data sets, there are indications thatthe prevalence of asthma also varies across in-dividual Hispanic origin groups (Kinnert et al.2002). For example, data from the HispanicHealth and Nutrition Survey (HHANES) indi-

* An earlier version of this article was presented atthe 2004 meetings of the Population Association ofAmerica, Boston, MA. The helpful comments ofParker Frisbie, five anonymous reviewers, and JHSBeditors Peggy Thoits and Eliza Pavalko are grateful-ly acknowledged. Address correspondence to EmilyRosenbaum, Fordham University, Department ofSociology, Dealy Hall 402C, Bronx, NY 10458 (e-mail: [email protected]).

cate that Puerto Rican children are more likelyto have asthma than their Cuban American andMexican American counterparts (Mendoza etal. 1991). The higher prevalence of asthmaamong Puerto Ricans is consistent with thefinding that the prevalence of lifetime asthmain 2001 was highest in Puerto Rico relative tothe other 53 reporting areas of the BehavioralRisk Factor Surveillance System (BRFSS)(Rhodes et al. 2003). Unfortunately, little isknown about the prevalence of asthma amongAsians, nor among specific Asian origingroups (Boardman et al. 2001).

Several risk factors for asthma have beenidentified. Exposure to tobacco smoke in thehome raises the risk of asthma, as does low so-cioeconomic status and metropolitan residence(Institute of Medicine 2000; Miller 2000; Shaw2004). Researchers have suggested that at leastpart of the effect of socioeconomic status is ac-counted for by the fact that economically dis-advantaged families are more likely to occupyinadequate housing (Boardman et al. 2001),and thus experience greater exposure than domore affluent families to the kinds of asthmatriggers—including dust mites, pet dander,cockroach and mice feces, and mold—that arefound in substandard housing (Bashir 2002;Kreiger and Higgins 2002; Rauh, Chew, andGarfinkel 2002). Moreover, the finding thatmetropolitan residence significantly predictsasthma among black but not white children hasbeen argued to reflect the segregation of manyblack children in deteriorated inner-city neigh-borhoods (Boardman et al. 2001) that are homealso to many sources of outdoor pollutants, in-cluding transfer stations, industrial land uses,and transportation depots (Gold and Wright2005). Such logic can be extended to accountfor the high incidence of asthma among PuertoRicans, who experience many of the same dis-advantaged housing and neighborhood envi-ronments (Rosenbaum 1996; Rosenbaum et al.1999). Persuasive evidence of the influence ofhousing and neighborhood conditions on asth-ma comes from the Moving to OpportunityDemonstration Program Boston site, wherechildren who moved from public housing inhigh-poverty neighborhoods to higher-qualityhousing in other neighborhoods experienced a50 percent drop in asthma attacks (Katz et al.2001).

RACIAL/ETHNIC DISPARITIES INHOUSING AND NEIGHBORHOODCONDITIONS

A long literature has documented the persis-tence of racial/ethnic disparities in housing andneighborhood conditions, net of socioeconom-ic status, that work to the disadvantage ofAfrican Americans, Puerto Ricans, and non-white Hispanics more generally. In terms ofhousing, blacks and Hispanics in New YorkCity are more likely than whites and Asians tooccupy housing units that suffer from numer-ous structural and maintenance deficiencies,such as interior and exterior leaks, chippingpaint and broken plaster, holes in the floors orwalls, and pest infestation (Rosenbaum andFriedman 2007). These housing conditions al-so appear to be more common among PuertoRicans and Dominicans than among Central orSouth Americans, and are more prevalentamong Hispanics reporting black than non-black race (Rosenbaum and Friedman 2007).The disproportionate exposure to substandardhousing experienced by blacks and Hispanicsalso holds on a national level, regardless ofwhether the household owns or rents its unit(Friedman and Rosenbaum 2004). The kinds ofhousing problems documented for blacks andHispanics, moreover, are precisely those thatcan give rise to asthma attacks (Bashir 2002;Krieger and Higgins 2002; Rauh et al. 2002).

With respect to neighborhood conditions, ithas long been recognized that the housingstock and the neighborhood amenities found inminority neighborhoods are of lower qualitythan those found in areas where whites pre-dominate (Massey, Condran, and Denton 1987;Massey and Denton 1993). Studies of the loca-tional attainment process have demonstrated ageneral pattern of access to advantaged areaswhereby whites experience the highest levelsof access, followed by Asians, Hispanics, andfinally blacks (Alba and Logan 1991, 1993).These patterns also hold in New York City,where blacks and Hispanics are more likelythan whites to live in neighborhoods withboarded-up buildings, and high crime andpoverty rates, and where residence in disad-vantaged neighborhood environments is moreprevalent among Puerto Ricans and Hispanicsreporting black race relative to non-PuertoRicans and non-black Hispanics (Rosenbaumand Friedman 2007). The fact that racial/ethnicdifferentials in housing and neighborhood con-ditions persist in the face of controls for so-

132 JOURNAL OF HEALTH AND SOCIAL BEHAVIOR

cioeconomic status and other individual-levelpredictors suggests that the disadvantages ex-perienced by Hispanics and, especially, blacksare largely the result of housing market dis-crimination (Turner et al. 2002; Yinger 1995).

THE MECHANISMS LINKINGHOUSING AND NEIGHBORHOODENVIRONMENTS TO ASTHMA

Researchers have identified three potentialpathways leading from environmental contextto asthma: differential exposure, stress, andhealth-related behaviors (Gold and Wright2005). For those environmental conditions rec-ognized as asthma triggers—such as damp-ness, dust mites, cockroach and rodent feceswithin the home, and excessive levels of pollu-tants outdoors—direct exposure is the mostlikely mechanism linking physical context andasthma. The housing market forces that limitthe choices of blacks, Puerto Ricans, and non-white Hispanics generally to badly maintainedhousing and disadvantaged neighborhoods,therefore, disproportionately expose thesegroups to a variety of asthma triggers.Controlling for this differential exposure maythus help to mediate observed differentials inasthma prevalence.

While exposure to indoor and outdoor pol-lutants reflects a direct relationship betweenenvironmental context and asthma, stress andhealth-related behaviors act as interveningvariables linking context and asthma. These in-direct pathways reflect a process whereby theenvironmental context increases the individ-ual’s vulnerability to asthma, which then in turnraises the likelihood of acquiring the condition.For example, when confronted with a stressfulsituation, the body releases a number of hor-mones that enhance the chance of surviving orovercoming the immediate, short-term threat(the “fight or flight” response). Yet whenstressful situations become chronic, these hor-mones overload the system and erode the ef-fectiveness of the immune system, thereby in-creasing the individual’s vulnerability to illness(Hill, Ross, and Angel 2005; Massey 2004).Living in neighborhoods characterized by highlevels of physical and social disorder has beenidentified as a chronic stressor, since residentsare constantly aware of the dangers surround-ing their homes (Downey and van Willigen2005; Wright et al. 2004). While residence nearindustrial activity may expose residents toasthma triggers, it can also increase vulnera-

bility to asthma onset by heightening stresslevels (Downey and van Willigen 2005).

Living in disadvantaged neighborhoods alsoincreases residents’ vulnerability to ill healthby affecting their behaviors. The fear and stressassociated with neighborhood crime and vio-lence may increase peoples’ tendency to smoke(Gold and Wright 2005; Wright et al. 2004).By causing people to fear for their safety,neighborhood crime and disorder may alsocause residents to spend more time indoors(Cagney and Browning 2004, 2005), and par-ents to exercise “protective parenting”(Furstenberg et al. 1999), thereby increasingtheir exposure to the asthma triggers insidetheir homes. Because the forces that create andmaintain high levels of racial/ethnic segrega-tion also concentrate poverty and its associatedproblems (including crime and disorder) in mi-nority neighborhoods (Massey and Denton1993), residents of such areas have a dispro-portionately higher vulnerability to disease asa result of heightened stress and potentiallyhealth-damaging behaviors (Massey 2004).

HYPOTHESES

In summary, substantial evidence demon-strates that blacks and particular Hispanic sub-groups are disproportionately likely to experi-ence substandard housing and neighborhoodenvironments. Such differentials parallel thosein asthma prevalence, suggesting that directexposure to substandard housing and neigh-borhood conditions, and the increased vul-nerability to ill health associated with theseconditions, may contribute to the observedvariation in asthma rates. I therefore expectthat residence in badly maintained and crowd-ed housing, and in areas with higher levels ofmanufacturing activity and greater concentra-tions of transportation facilities, will be asso-ciated with higher levels of asthma prevalence,largely because of the concomitant higher de-gree of exposure to asthma triggers. Living inhighly segregated areas and those with highcrime rates, and perceiving a low level of so-cial cohesion in one’s neighborhood will alsobe positively associated with the prevalence ofasthma, but these two forces will more likelyoperate through the indirect routes of higherstress and negative health-related behaviors.Finally, and most importantly, I expect thatcontrolling for these aspects of the environ-mental context will mediate the observedracial/ethnic disparities in asthma, and thus

HOUSING AND ASTHMA 133

cause the observed disparities to lessen in sizeor disappear altogether. Should this last hy-pothesis be supported, then housing marketdiscrimination—the presumed root of housingand neighborhood inequalities—can be con-sidered as a contributing cause of such dispar-ities (cf. Cain and Kington 2003) and a keymechanism by which race/ethnicity is a “fun-damental cause” of disease (Link and Phelan1995).

DATA AND METHODS

Data

The primary source of data is the 2002 pan-el of the New York City Housing and VacancySurvey (HVS), a multistage probability sampleof approximately 16,000 occupied housingunits located throughout the five counties, orboroughs, which make up the city. The HVS isconducted every two or three years by theCensus Bureau under contract to the City ofNew York, in compliance with state and locallaws regarding rent control. The 2002 panel isbased on a sample drawn from the 2000 Censusaddress list. As an inter-censal survey contain-ing a wealth of information, the HVS is thebest source of up-to-date information concern-ing the city’s population and its housing. Aftereliminating cases with missing data on the de-pendent and independent variables, the analyt-ical data set contains 12,058 households.1

A number of new questions were added tothe 2002 panel that extends the HVS’s reach in-to the sphere of health. Of importance here is aquestion that asked whether there is anyone inthe household who has been told by a doctor orother health professional that she or he hasasthma. This question likely underestimatesdifferences in the prevalence of asthma, giventhat findings from a census of 1,982 childrenunder 16 in Central Harlem conducted by theHarlem Children’s Zone Asthma Initiative re-vealed a level of asthma more than four timeshigher than the national level of childhoodasthma: Fully 30.2 percent of the children as-sessed were either diagnosed with asthma orexhibited asthma-related symptoms, includingmany who had never received a formal diagno-sis of asthma (Nicholas et al. 2005). This firstquestion was followed up with the interviewerasking how many such people live in thehousehold. As a result, the specific individualswith asthma are not identified, and thus it is un-clear if those persons are children, adults, orboth. Because almost 85 percent of households

report no asthmatics, the dependent variable inthe analysis is a dichotomy that is coded 1when there is at least one asthmatic present inthe household and 0 when no asthmatic ispresent.

The analysis focuses on three key sets of in-dependent variables: race/ethnicity, housingconditions, and neighborhood conditions. Tencategories of (self-reported) race/ethnicity areused: non-Hispanic white, non-Hispanic black,Puerto Rican, Dominican, Central/SouthAmerican, Mexican, other Hispanic, Chinese,Asian Indian, and other Asian. I omit from theanalysis the very few non-Hispanic householdsheaded by someone reporting more than onerace (0.5 percent of all households). In the mul-tivariate analysis, non-Hispanic whites areused as the reference group.

With respect to housing conditions, four in-dicators are used. The first is the number ofmaintenance deficiencies in the housing unit,which is a summary index of seven possibledeficiencies reported by the respondent for thethree months preceding the survey. These defi-ciencies are: toilet breakdowns; heating break-downs; the need for additional heat; the pres-ence of rats or mice; leaks from the outside;cracks or holes in the floors, walls, or ceiling;and large areas of broken plaster. Three di-chotomous variables (coded 1 if the conditionis present, 0 otherwise) are used to differenti-ate housing units with no deficiencies (the ref-erence category) from those with only one ortwo, or three or more, of these deficiencies.

The second indicator of housing conditionsis crowding (coded 1 if there is at least one per-son per room, and 0 otherwise), and the third istenure status (coded 1 for renters, 0 for own-ers). Crowding is important to examine since ithas been associated with poorer physical andmental health (Edwards et al. 1994; Gove,Hughes, and Galle 1979), and with elevatedlevels of cockroach allergens (Leaderer et al.2002). As an indicator of substandard housingconditions, crowding is expected to be posi-tively associated with the prevalence of asth-ma. Renter households are expected to exhibita higher prevalence of asthma than are ownerhouseholds, in part because of the generallylower quality of rental housing and the lowersocioeconomic profile of renters versus own-ers. The final housing condition is a dichotomydifferentiating between households in which atleast one smoker is present from those free ofresident smokers. Because indoor tobacco


smoke can trigger asthma onset and asthma at-tacks, exposure to smokers in the home shouldbe positively associated with asthma preva-lence.

A total of six indicators of neighborhoodconditions are used. Two are measured at theindividual level but reflect conditions in thebroader area. The first of these, a dichotomyindicating whether there are any boarded-upbuildings within 300 feet of the sampled hous-ing unit, derives from an interviewer observa-tion. While the presence of boarded-up build-ings and the physical decay they signal may re-flect the presence of asthma triggers, they alsoindicate social disorder, and thus may makearea residents more vulnerable to disease byheightening their stress levels or altering theirbehaviors.

The second measure taps into the amount ofsocial cohesion in the neighborhood, as per-ceived by the respondent. This variable derivesfrom the mean score on responses (stronglyagree to strongly disagree, coded 1 to 4) fromtwo items often used in scales measuring socialcohesion: “People in this neighborhood can betrusted” and “People in this neighborhood arewilling to help their neighbors.” The variableused in the analysis is a dichotomy, coded 1 forhouseholds reporting little or no social cohe-sion (those with a mean score of 2.5 or aboveon the two items) and 0 otherwise. Although ameasure obtained for the aggregate level wouldhave been preferable as an indicator of limitedsocial cohesion, the householder’s perceptionwill directly shape his or her own behavior andfamily management techniques. As a result,this perception may serve as a proxy for feel-ings of stress deriving from living in a threat-ening environment. Like the presence ofboarded-up buildings, then, the perceived lackof social cohesion will also be positively asso-ciated with asthma prevalence by increasingvulnerability.

The final four indicators of neighborhoodconditions are measured for the 55 subareasidentified in the HVS. The HVS subareas aregeographic units comprised of entire Censustracts with a minimum population of 100,000in accordance with Census Bureau rules onconfidentiality. Although larger than whatmost researchers consider “neighborhoods,”subareas are very similar to the 59 communitydistricts in New York, and thus are meaningfulgeographic units for service delivery and poli-cy making. I used Infoshare to create the sub-

area indicators. Infoshare is a unique data basecontaining public and private sources of datafor New York that allows users to aggregate da-ta up to a specified number of geographies, in-cluding the HVS subarea.

The first subarea measure, the percentwhite, derives from 2000 Census data. Percentwhite is used to control for the high degree ofracial/ethnic segregation in New York City andwas chosen over percent black because of theemergence of large swaths of predominantlynon-white areas composed almost exclusivelyof blacks and Hispanics (Alba et al. 1995;Lobo, Flores, and Salvo 2002). The secondsubarea measure, the 2000 violent crime rate(measured per 1,000 population), uses thenumber of violent crimes against individuals(i.e., murder, rape, assault, robbery) originallyreported by the New York City Police Depart-ment. Because segregation concentrates pover-ty and its correlates in predominantly minorityneighborhoods, residents of such areas experi-ence higher levels of chronic stress and are thusmore vulnerable to disease than are residents in“whiter” neighborhoods (Massey 2004).Similarly, residence in violent and dangerousneighborhoods increases stress levels and mayalter residents’ behaviors, thereby heighteningresidents’ vulnerability (Wright et al. 2004). Asa result, the prevalence of household-levelasthma should rise along with the crime ratebut fall as percent white rises.

The final two subarea conditions are hy-pothesized to influence asthma prevalencethrough the mechanism of exposure. Thesemeasures derive originally from data from theNew York City Department of Finance on landuse (for 1999). The first is the number of trans-portation facilities (such as bus maintenancedepots, train yards, and ferry terminals) per100,000 population, and the second is the num-ber of light and heavy manufacturing facilitiesper 100,000 (both use 2000 Census counts inthe denominator). The concentration of trans-portation facilities in an area will give rise tohigh levels of pollutants from idling buses andtrucks, and heavy vehicular traffic more gener-ally. Exposure to mobile sources of pollutantssuch as these may have more profound conse-quences for residents’ health than stationarysources (Morello-Frosch 2006), such as theoutput stemming from manufacturing activi-ties. Therefore, I expect that residence in areaswith high concentrations of either type of ac-


tivity—manufacturing or transportation uses—will be associated with higher levels of asthma.

The remaining independent variables in-clude indicators of household composition andsocioeconomic status (age of householder, ed-ucational attainment of householder, house-holder gender, total household income/10,000,and household receipt of Temporary Assistanceto Needy Families [TANF]) that affect wherepeople live. I use these household characteris-tics mainly as statistical controls. I also usecontrols for the number of children ages 0through 12 and the presence of teenagers (chil-dren ages 13 through 17) to account for groupdifferences in household size. This is essentialgiven that the particular asthmatic is not iden-tified by the dependent variable, and becausethe risk of having an asthmatic in the house-hold is directly affected by the size and agecomposition of the household.2 Additionalcontrols include nativity status of the house-holder (coded 1 for foreign-born householdersand householders born on the island of PuertoRico,3 0 otherwise), given the importance ofimmigration in New York City and the likeli-hood of a “healthy immigrant” effect (cf.Kinnert et al. 2002). The final control variableis a measure of the length of time householdshave occupied their housing units. This lastvariable is a dichotomy coded 1 for householdswho have lived in their units for five or moreyears, and 0 otherwise. Controlling for dura-tion in place is essential for mitigating some ofthe limitations of cross-sectional data for thistype of analysis.

Methods

The clustering of households within subar-eas suggests the need for multi-level modelingtechniques. This need is validated by the intra-class correlation coefficient (ICC) for subareas(level 2) calculated from the null model, whichindicates that subareas account for about 16percent of the variation in household-level asth-ma. As a result, the model is estimated usingmultilevel logistic regression techniques avail-able in the SAS procedure, “proc glimmix.”

RESULTS

Descriptive Analysis

Table 1 shows two sets of percentages: the“raw” percentage of households with at leastone asthmatic (by race/ethnicity), calculateddirectly from the data, and the “adjusted” per-

centages, which have been standardized by ap-plying the distribution of white households bynumber of children ages 0 through 17 to thehousehold-size-specific asthma rates exhibitedby each nonwhite group. The adjusted percent-ages are therefore free of much of the distor-tion arising from group differences in house-hold size, and thus improve the validity ofcross-group comparisons.

Regardless of the measure used, the data re-veal a huge range in the experience of asthma.Focusing on the adjusted percentages, PuertoRican households are the most likely to containat least one asthmatic, and they are more than2.5 times more likely to do so compared towhite households. All other Hispanic house-holds report lower levels of asthma relative toPuerto Rican households, a finding consistentwith those of other studies (Kinnert et al. 2002;Mendoza et al. 1991). The low level of adjust-ed asthma prevalence among Mexican house-holds—less than half that among white house-holds and less than one-fifth that among PuertoRican households—suggests additional evi-dence of the “epidemiological paradox” char-acterizing this group, namely, fairly robusthealth conditions in the face of low socioeco-nomic status (Boardman et al. 2001; Kinnert etal. 2002). Black households are also more like-ly to contain an asthmatic than are white house-holds, while Chinese and Asian Indian house-holds are less likely to do so.


TABLE 1. Percent of New York City HouseholdsThat Contain at Least One Asth-matic, by Race/Ethnicity of House-holder, 2002

Race/Ethnicity Adjustedof Householder Raw Percent Percenta

Non-Hispanic white 11.07Non-Hispanic black 18.88 15.82Puerto Rican 32.85 28.02Dominican 17.78 14.78Central/South American 16.04 13.28Mexican 10.47 5.05Other Hispanicb 17.52 16.83Chinese 7.83 6.81Asian Indian 9.02 7.32Other Asianc 12.46 11.72a The distribution of the number of children younger than18 in the household for whites is used to adjust the per-centages.b Category includes Cubans and “other” Hispanics.c Category includes Koreans, Japenese, Filipinos,Vietnamese, other Pacific Islanders, and “other” Asians.

To what degree is the variation in asthmaprevalence associated with differential expo-sure to potentially adverse housing and neigh-borhood conditions? To lend insight to thisquestion, Table 2 presents descriptive statisticsfor the housing, neighborhood, and subareaconditions for each racial/ethnic group (house-hold characteristics can be found in theAppendix). Starting with the housing charac-teristics, crowding is most extreme amongMexican, Dominican, Central/South Ameri-can, and Asian Indian households, a findingundoubtedly related to the fact that the vast ma-jority of these households are headed by immi-grants (Appendix). The presence of smokers inthe household is highest among Puerto Ricans,other Asians, and other Hispanics, while allHispanic (apart from Central/South American)and black households are most likely to live inunits plagued by three or more maintenancedeficiencies. Finally, white and Chinese house-holds are least likely to be renters, while homeownership is extremely rare among Mexicanand Dominican households.

Turning to the neighborhood characteristics,Puerto Ricans are most likely to report little orno social cohesion in their neighborhoods,with just over 30 percent doing so, followed by

Central/South Americans, African Americans,and Dominicans, at about 24 percent. AfricanAmericans are most likely to live near board-ed-up buildings (16 percent), followed byMexicans and Puerto Ricans (14 and 11 per-cent, respectively). In contrast, whites live inneighborhoods with the highest levels of socialcohesion (as reported by the respondent), andalong with all Asian and Central/SouthAmerican households, they also live in neigh-borhoods with the fewest signs of physicaldecay.

Given the persistence of extremely high lev-els of white/black segregation in New YorkCity (Rosenbaum and Argeros 2005), it ishardly surprising that the mean percentage ofwhites in the subarea is lowest among blackhouseholds. Puerto Rican and Dominicanhouseholds also tend to live in subareas withfar fewer white residents relative to the subar-eas in which whites, Asians, and the remainingLatino groups reside. Somewhat unexpectedly,white households live in subareas with thehighest average exposure to transportation fa-cilities, followed by Chinese, Puerto Rican,and Mexican households. In contrast, Central/South Americans experience the highest aver-age exposure to manufacturing facilities at the


TABLE 2. Selected Descriptive Characteristics of New York City Households, by Race/Ethnicity ofHouseholder, 2002 (percentages)

Race/Ethnicity of Householder

Housing characteristics—Crowded 17.52 23.95 24.61 40.42 43.31 74.90 22.29 37.32 47.15 36.29—At least one smoker 23.68 24.61 32.49 13.15 17.42 17.86 29.10 21.12 17.29 29.33—Number of maintenance deficiencies——None 63.72 44.47 41.65 35.44 49.39 37.80 52.42 63.88 54.02 61.18——One or two 29.73 36.70 38.50 42.57 36.51 42.13 34.16 28.03 36.05 29.03——Three or more 6.55 18.83 19.86 22.49 14.10 20.06 13.42 8.08 9.93 9.79—Renter 56.16 69.96 84.72 92.62 79.29 97.41 71.98 55.45 64.73 68.83Neighborhood characteristics—Little/no social cohesion 7.44 23.76 30.49 23.38 24.34 18.99 21.23 14.85 12.51 13.43—Boarded-up buildings nearby 4.00 16.00 11.00 9.00 6.00 14.00 9.00 3.00 2.00 7.00Subarea characteristics—Percent white (mean) 63.75 24.09 35.43 31.43 42.24 43.01 47.31 53.75 47.13 51.14—Transportation facilities/100,000 (mean) 570.33 221.57 405.79 279.22 294.00 403.81 337.98 473.63 243.65 339.70—Manufacturing facilities/100,000 (mean) 31.09 31.07 51.07 38.94 60.32 55.59 42.64 34.29 47.38 56.58—Crime rate (mean) 9.17 9.57 11.00 9.97 10.25 11.16 9.47 10.08 8.83 10.12

N of cases 5,238 2,863 1,130 665 615 149 236 429 344 388

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subarea level, followed by Dominican,Mexican, and Puerto Rican households.Finally, the subareas in which Mexican andPuerto Rican households live exhibit the high-est average violent crime rates.

In summary, the descriptive analysis indi-cates preliminary support for the notion thathousing and neighborhood conditions may me-diate racial/ethnic disparities in asthma preva-lence, in that the groups most afflicted by asth-ma tend to live in less-desirable circumstancesthan do the groups least affected by the illness.This is particularly the case for Puerto Ricans,who exhibit the most extreme levels of asthmawhile often living in the worst housing and inneighborhoods where exposure to environmen-tal triggers is high, and where social disorderand crime may induce chronically high stresslevels or behaviors with the potential to harmone’s health. However, to fully determine theextent to which housing and neighborhoodconditions account for observed racial/ethnicvariation in asthma, we must turn to multivari-ate analysis.

Multivariate Analysis

Table 3 presents the results of three multi-level logistic regression models (with randomintercepts and fixed slopes). Model I includesthe racial/ethnic dummy variables along withthe indicators pertaining to the number/pres-ence of children of different ages. This modelprovides significance tests for the adjusted per-centages shown in Table 1, and establishes abaseline against which the effects of race/eth-nicity in the full models can be compared. Theresults indicate that black, Puerto Rican,Dominican, and other Hispanic households areall significantly more likely than white house-holds to contain at least one asthmatic, net ofthe controls for household size and age com-position. Similarly, the lower prevalence ofasthma among Chinese and Asian Indianhouseholds in Table 1 attains significance,while that among Mexican households doesnot.

Model II, the first full model, uses the sum-mary index of maintenance deficiencies de-scribed above. Looking first at the results forthe household characteristics, we see clear sup-port for expectations of a “healthy immigrant”effect; the odds of asthma are about 31 percentlower in immigrant than native-born house-holds. Female headship, however, significantlyraises the odds of asthma. This variable is prob-

ably a better indicator (relative to the otherhouseholder characteristics) of the socioeco-nomic status of all members of the household,and thus likely reflects the negative associationof asthma with socioeconomic status at the in-dividual level, as reported by others (e.g.,Boardman et al. 2001; Miller 2001). As ex-pected, the odds of having at least one asth-matic present in the household significantlyrise with the number of children and teens inthe household.

The results for the housing, neighborhood,and subarea characteristics reveal solid supportfor hypotheses concerning the potentially ad-verse effects of deteriorated housing andthreatening neighborhoods. With respect tohousing conditions, the odds of asthma risesubstantially along with the number of mainte-nance deficiencies, suggesting that direct ex-posure to higher concentrations of asthma trig-gers escalates household members’ risk for thecondition. This effect is quite substantial; rela-tive to living in a unit that is free of any type ofmaintenance deficiencies, living in a unit withone or two problems raises the odds of asthmaby just over 44 percent, while living in a moredeteriorated unit almost doubles the odds ofasthma. With respect to the neighborhood andsubarea effects, perceiving little or no socialcohesion in one’s neighborhood significantlyraises the odds of asthma by almost 17 percent,and living in subareas with higher concentra-tions of transportation facilities also bears apositive and significant relationship to the oddsof asthma among household members. Thislatter effect, however, is quite small.

Model III replicates Model II but replacesthe summary index of maintenance deficien-cies with dichotomies indicating the presence(during the three months preceding the survey)of each of the seven conditions used to createthe index. The purpose of this substitution is toevaluate if all or only a subset of the measuredconditions significantly predict the prevalenceof asthma. As would be expected, the resultsfor the other variables in the basic full modelremain largely unchanged by this substitution.Four of the seven conditions significantly raisethe odds of asthma in the household. For ex-ample, households living in units that experi-enced heating breakdowns or needed addition-al heat in the preceding three months are about19 and 18 percent, respectively, more likely tocontain an asthmatic than those without heat-ing problems. Similarly, the odds of house-


hold-level asthma are more than 25 percenthigher when housing units are infested with ro-dents, and they are more than 35 percent high-er when housing units experience leaks fromthe outside, relative to when the housing is free

of rodents and is watertight. These results,then, are consistent with prior reports of the in-fluence of dampness, lack of heat, and rodentinfestation on the risk of asthma (Bashir 2002;Kreiger and Higgins 2002; Rauh et al. 2002;


TABLE 3. Results of Multilevel Logistic Regression Models Predicting the Prevalence of Asthma inNew York City Households, 2002 (odds ratios; N = 12,058)

Predictors (Level I) Model I Model II Model III

Race/ethnicity (vs. white)—Black 1.574*** 1.329** 1.329**—Puerto Rican 3.369*** 2.894*** 2.909***—Dominican 1.301* 1.332* 1.326*—Central/South American 1.187 1.342* 1.342*—Mexican .681 .836 .862—Other Hispanic 1.616** 1.783** 1.762**—Chinese .586** .749 .751—Asian Indian .583** .803 .809—Other Asian 1.032 1.222 1.225Household characteristics—Foreign-born householdera .707*** .701***—Householder age 1.002 1.002—Householder education (vs. some college or more) ——Less than high school .979 .978——High school diploma .939 .936—Female householder 1.261*** 1.269***—Number of children 0–12 (vs. none)——One 1.667*** 1.649*** 1.661***——Two or more 2.345*** 2.363*** 2.344***—Presence of children 13–17 1.659*** 1.632*** 1.625***—Household income/10,000 1.006 1.006—Receives TANF 1.321 1.292—Lived in unit 5+ years 1.090 1.088Housing characteristics—Crowded .996 .986—At least one smoker 1.119 1.115—Number of maintenance deficiencies (vs. none)——One or two 1.442***——Three or more 1.931***—Type of maintenance deficiency——Toilet breakdowns 1.087——Heating breakdowns 1.185*——Need additional heat 1.178*——Presence of mice or rats 1.254***——Cracks/holes in walls, floors, ceiling 1.142——Large patches of missing plaster .917——Leaks (from inside) 1.343***—Renter .979 .979Neighborhood characteristics—Little/no social cohesion 1.169* 1.160*—Boarded-up buildings nearby 1.064 1.046Predictors (Level II)Subarea characteristics—Percent white .998 .998—Transportation facilities/100,000 1.000* 1.000*—Manufacturing facilities/100,000 1.000 1.000—Crime rate 1.003 1.004

Intercept 2.142*** .073*** .076***–2 log likelihood 9817.9*** 60771.5*** 60740.4***d.f. 12 31 36

* p ≤ .05; ** p ≤ .01; *** p ≤ .001Note: TANF = Temporary Assistance for Needy Families.a Includes householders born on the island of Puerto Rico.

Shaw 2004). Yet not only do these conditionsindividually increase the risk of asthma, butwhen found together (as shown in Model II),the risk of asthma is greatly magnified.

As theorized earlier, the deleterious conse-quences of perceiving a lack of social cohesionamong one’s neighbors may reflect heightenedstress or the use of defensive behaviors, such asremaining indoors, which may exacerbate ex-posure to indoor triggers. To test this hypothe-sis, two additional models were estimated (re-sults available upon request). The first includesfixed interactions between low perceived so-cial cohesion and the presence of smokers, andbetween social cohesion and the number ofmaintenance deficiencies. The second modelreplaces the latter interactions with terms in-teracting low perceived social control witheach of the four significant individual mainte-nance conditions (heating breakdown, need foradditional heat, presence of rodents, and leaksfrom the outside). No interactions in the firstmodel emerged as significant, and in the sec-ond model there was only one (social cohesionx leaks from the outside, p < .05). This inter-action, though, was negative in direction, sug-gesting that perceptions of limited trust andsafety among one’s neighbors moderates thehigher risk for asthma associated with this as-pect of substandard housing. This result sug-gests that perceiving limited support and po-tential dangers in the surrounding neighbor-hood may impact asthma by increasing stress,rather than by compounding exposure to asth-ma triggers. Such an interpretation is consis-tent with the finding that stress mediates someof the effect of community violence on resi-dents’ asthma (Wright et al. 2004).

Finally, controlling for the full range of vari-ables reduces the extent of excess asthma mor-bidity among black and Puerto Rican house-holds by 43 and 20 percent, respectively. Thisfinding suggests that at least part of the excessmorbidity experienced by black and PuertoRican households in New York City is ac-counted for by the racial/ethnic disparities inhousing and environmental conditions thatwork to the disadvantage of these groups.Additional models that enter each block ofvariables along with the race/ethnicity di-chotomies (available upon request) indicatethat the sets of housing, neighborhood, andsubarea characteristics individually act to me-diate the disproportionately high levels of asth-ma among blacks and Puerto Ricans, with the

set of housing conditions having the greatestmediating effect.

While controlling for the full range of pre-dictors lessens the extent of excess asthmamorbidity among two of the most badly affect-ed groups, the same is not true for othergroups. Specifically, the higher odds of asthmaamong Dominican and other Hispanic house-holds evident in Model I remain significantand increase slightly in size in both full mod-els, while the effect associated withCentral/South American households becomesboth larger and statistically significant.Similarly, controlling for the full set of predic-tors eliminates the statistically significant ad-vantage in asthma prevalence among Chineseand Asian Indian households, and this alsobrings the odds of asthma among these groupscloser to those exhibited by whites. Additionalanalyses suggest that these effects result fromthe beneficial influence of foreign birth. Oncethe advantage of being an immigrant house-hold is removed, the odds of having at least oneasthmatic in the household rises for thosegroups with a high representation of the for-eign born.

In summary, the analysis demonstrates thatneighborhood and especially housing charac-teristics exhibit expected relationships with theprevalence of asthma at the household level. Ingeneral, households living in deterioratedhousing units—especially those that are damp,infested with rodents, and lacking sufficientheat—and who perceive little or no social co-hesion in their neighborhoods are more likelythan other households to contain at least oneasthmatic. These housing and neighborhoodeffects, moreover, are independent of influenceof socioeconomic status. However, even in thepresence of a full range of available controls,black and, especially, Puerto Rican householdsremain far more likely to contain asthmaticsthan do white households. Of equal impor-tance, the full set of controls accounts for lessthan half the effect for blacks, and only one-fifth the effect for Puerto Ricans. Thus, where-as housing and neighborhood conditions areimportant predictors of asthma prevalenceamong households in New York City, and helpto explain at least part of the higher asthma lev-els for blacks and Puerto Rican Hispanics,these groups remain far more likely to sufferfrom this aspect of ill health than comparablewhite households.


DISCUSSION

The goal of this article was two-fold. Thefirst was to document differentials in the preva-lence of asthma for households from ten spe-cific racial/ethnic groups. While other studieshave examined differences between blacks andwhites (e.g., Boardman et al. 2001; Miller2001) and there has been some investigation ofdifference among Hispanic groups (Kinnert etal. 2002; Mendoza et al. 1991), little researchhas been done to date that includes Asians.Using data for New York City, I find significantvariations in the prevalence of asthma, withPuerto Rican households most likely andChinese, Asian Indian, and Mexican house-holds far less likely to contain at least one asth-matic, even when group differences in house-hold size are taken into account.

The second goal was to establish if differen-tial exposure to potentially adverse housingand neighborhood conditions helped to explainat least part of the observed racial/ethnicdisparities in asthma prevalence. Multilevel lo-gistic regression models demonstrated thathousing and neighborhood conditions weresignificantly associated with the presence of atleast one asthmatic in the household. As ex-pected, the odds of asthma prevalence rosestrongly with the number of maintenance defi-ciencies reported by householders; were en-hanced by dampness, lack of adequate heat,and by the presence of rodents; and were high-er for households living in areas perceived tolack the safety and security of a socially cohe-sive neighborhood. Additional tests suggestthat this latter result may partly reflect the in-creased vulnerability to asthma that resultsfrom increased stress or behaviors that increaseexposure to indoor triggers, but the evidence isweak at best. To differentiate between the twomechanisms requires psychosocial measuresand measures of everyday behaviors that arenot available in this data set.

The findings regarding the influence ofhousing conditions, and especially the roleplayed by maintenance deficiencies, point tothe importance of strengthening both the lawsregarding the correction of housing code viola-tions and the programs aimed at helping land-lords to make structural improvements in theface of high costs. Interventions, such as thoseembodied in “Healthy Homes” programs thatprovide comprehensive services, including thereduction of asthma triggers in the home, canhelp to reduce the severity of asthma (Krieger

and Higgins 2002; Kreiger et al. 2005;Nicholas et al. 2005). The potential importanceof this type of intervention is underscored bythe fact that the set of housing conditions wasmost strongly associated with the excess asth-ma morbidity exhibited by black and PuertoRican households, and by the fact that the im-pact of these conditions relative to the otherpredictors is quite substantial. That is, whilethe standardized coefficients for Model III (notshown) reveal that race/ethnicity and house-hold composition and headship have, by far,the largest effects on the prevalence of asthma,the presence of leaks has an effect that exceedsthat of female headship (the weakest of the for-mer set of variables). Moreover, the effects ofheating breakdowns and needing additionalheat also outweigh those of the majority ofvariables in the model. Thus, efforts to elimi-nate the indoor triggers arising from housingdeterioration have great potential to not onlyreduce asthma rates generally, but also to re-duce the excess morbidity exhibited by blacksand Puerto Ricans.

A growing body of research, however, indi-cates that community-based health interven-tions implemented by governmental agenciesat any level have the greatest possibility of suc-cess when they involve inexpensive and easy-to-sustain changes in behavior, when they in-volve a personal dimension such as home vis-its by community health workers, and whenthey take into account the complex cultural, so-cial, and political nature of the immediate con-text (Corburn 2002; Green et al. 2002; Saegertet al. 2003). The latter is most efficiently ac-complished by involving local residents asequals in the planning and execution of a pro-gram, since only they can help researchers andscientists design interventions that make sensein, and can be easily incorporated into, com-munity residents’ daily lives (Corburn 2002).

Perhaps most significantly, I found that con-trolling for substandard housing and neighbor-hood conditions helped to account for a portionof the disadvantage experienced by black andPuerto Rican households. As the dispropor-tionate exposure of black and Puerto Ricanhouseholds to such conditions stems not fromlow socioeconomic status but from externalconstraints on their housing choices (Rosen-baum and Friedman 2007), discrimination inthe housing market can be said to contribute tothese groups’ lower levels of health. Such afinding adds to the growing literature docu-


menting a connection between racism andhealth (e.g., Williams and Collins 1995), and itpoints to the need to strengthen fair housinglaws and their enforcement to, once and for all,make access to safe and desirable housingavailable to all households.

Inevitably, though, the limitations inherentto this study may also contribute to findings ofvarying risks of asthma net of all predictors. Inaddition to the need for measures to differenti-ate between the potential indirect pathways, aclear limitation is the absence of information inthe data set on health behaviors and other char-acteristics of individual household members,as well as other factors relating to a predispo-sition to respiratory ailments. It is also possiblethat the unique circumstances of New YorkCity, with its persistently high levels of segre-gation and its extremely high levels of asthma,contribute to the findings of persistent differ-

entials. To better understand the disproportion-ate asthma risks faced by Puerto Ricans andblacks, it is essential that data sets containingall potentially relevant predictors be collectedfor a variety of different areas, and for individ-uals (rather than households).

The fundamental nature of race/ethnicity asa cause of disease (Link and Phelan 1995), soclearly evident here, means that any interven-tion that focuses only on the mechanisms link-ing physical context and disease will never betruly effective. Rather, until the social, eco-nomic, and political forces (including housingmarket discrimination) that maintain theracial/ethnic hierarchy of access to healthy en-vironments are dismantled, morbidity and mor-tality rates will continue to be disproportion-ately higher for those at and near the bottom ofthe hierarchy.


NOTES

1. Almost 14 percent of all occupiedhousing units have missing data onthe dependent variable, the presenceof any asthmatics within the house-hold, and on a theoretically key pre-dictor, the presence of smokers. Thedegree of missing data for these vari-ables likely derives from prematurelyended interviews, as these items are atthe end of the questionnaire, follow-ing such sensitive questions as in-come and public assistance receipt.Households with missing data onasthma prevalence are more advan-taged than are those with valid asthmainformation (with higher incomes anda higher degree of home ownership,for example), and they are more like-ly to be white. Thus, if full informa-tion had been available on asthmaprevalence, it is likely that the ob-served racial/ethnic differentialswould have been even greater.

2. The number of children of differentages was chosen as the indicator ofhousehold size (rather than the totalnumber of household members) giventhe variation in asthma prevalence byage. Data from the 2002 NationalHealth Interview Survey show thatlifetime asthma prevalence is higherfor those under 18 than for those over18, and it peaks in the 15 to 19 agegroup (Centers for Disease Control2002).

3. Although island-born Puerto Ricansare not immigrants, research has not-ed differences in health favoringPuerto Ricans born on the island rela-tive to those born on the mainland(e.g., Landale, Oropesa, and Gorman2000).

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