An ecological approach to understanding youth smoking trajectories: problems and prospects

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Blackwell Science, Ltd

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Original Article

Ecological approach to understanding youth

smoking trajectoriesPamela Wilcox

Correspondence to:

Pamela WilcoxDepartment of SociologyUniversity of Kentucky41531 Patterson Office TowerLexingtonKY 40506–0027USA

E-mail: [email protected]

RESEARCH REPORT

An ecological approach to understanding youth smoking trajectories: problems and prospects

Pamela Wilcox

University of Kentucky, Lexington, KY, USA

ABSTRACT

Non-random patterns of aggregate youth smoking rates and trajectories acrosscommunities suggest that individual-level characteristics cannot account fullyfor the behavior in question. Instead, at least part of the explanation must liesomewhere within the community context. Such community-level contextualeffects can impact directly both group and individual-level behavior (e.g. maineffects), and they can also condition the effects of individual-level factors onindividual behaviors (e.g. moderating effects). This paper reviews previousresearch examining community-level contextual effects regarding smoking andsubstance use more generally and identifies important limitations of this extantwork, thus defining an agenda for future empirical studies. Next, the (in)com-patibility of previous empirical findings with current theoretical models is dis-cussed. In offering an alternative to these existing models, the paper concludeswith presentation and discussion of a multi-level, integrated model of adoles-cent smoking trajectories. In this model, community/institutional forces arepresumed to impact smoking above and beyond individual-level main effects.These posited community-level forces are broad and varied, representing schoolcharacteristics, neighborhood demographic characteristics, religious culture,media influence, economic context, health services and so on. In addition toexhibiting contextual main effects, the effects of community in the proposedmultilevel model can be mediated by community-level processes, including theprocesses of control and socialization discussed herein. Also, community-levelcharacteristics may interact in producing certain tobacco-use outcomes and,perhaps most importantly, they may moderate or condition the effects of inter-individual differences on smoking.

KEYWORDS

Commmunity, context, smoking.

INTRODUCTION

The purpose of this paper is to provide rationale for anintegrative, ecological model of youth smoking traject-ories. Human ecology focuses on ‘the relationshipbetween the environmental (including the social andeconomic) and the demographic characteristics of thepopulation and the impact of these two broad sets of vari-ables upon human behavior’ (McBride & McCoy 1981,p. 284). More specifically, the focus is on the environ-mental influences at the community level and the

intracommunity–institutional level, including both themain effects as well as the moderating or conditioningeffects of such contexts.

The perspective invoked here presumes that theindividual is not the only unit of analysis of importancein understanding youth smoking trajectories. Rather,aggregate-level rates of smoking (i.e. among a cohort) atvarious stages of the life course and aggregate trajectoriesof tobacco use are also important phenomena—socialfacts ‘distinct from their individual manifestations’(Durkheim 1895/1938, p. 7; see also Wicker 1979;


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Altman 1990). In fact, epidemiological research showsclearly that there appear to be persistent, non-randompatterns of smoking across communities defined by geo-graphical areas and groups defined by gender, race, edu-cation level, income, occupation and marital status(Syme & Alcalay 1982). When such non-random pat-terns of aggregate rates and trajectories exist, it is doubt-ful that individual-level characteristics can account fullyfor the behavior in question; at least part of the explana-tion must lie somewhere within the collective itself. Con-textual effects must be considered. These contextualeffects can impact directly both group and individual-level behavior (e.g. main effects), and they can also con-dition the effects of individual-level factors on individualbehaviors (e.g. moderating effects).

Providing the rationale for a community-level per-spective allowing for such main and moderating contex-tual effects unfolds over the course of four major sectionswithin this paper. First, a working definition of commu-nity context is provided. Following this discussion, thesecond major section reviews empirical work addressingcommunity-level variation in smoking, including thatexamining variation in smoking across intracommunityinstitutions such as schools and churches. Next, thetheoretical rationale for community effects on adolescentsmoking is addressed, with a particular emphasis on thedisconnect between empirical evidence and theoreticaldevelopment in the field currently. The final section of thepaper highlights the new directions in which the fieldmust move in order to adequately understand commu-nity-level contextual effects on adolescent smoking. Thissection includes a proposed multilevel model of smokingand a brief discussion of how this model might be testedempirically.

COMMUNITY CONTEXT

Health behaviors, including youth smoking, are studiedtypically at the individual-level, and the motivationalforces that affect such behaviors are thought commonlyto lie either within the individual or within contexts veryproximal to the individual (e.g. families and peer groups).Regardless, there is some evidence, reviewed later, that weneed to consider contexts more distal to the individual,including the community in which the individual isembedded.

Gephart (1997, p. 9) defines neighborhoods and com-munities as ‘ . . . the immediate social context in whichindividuals and families interact and engage with theinstitutions and societal agents that regulate and controlaccess to community opportunity structures andresources. Neighborhoods are spatial units, associationalnetworks and perceived environments.’ In arguing

against the use of geographic/spatial units for definingcommunity and in recognizing that associational net-works may form but individuals within a communityneed not

know

one another, Darling & Steinberg (1997,p. 121) define community as ‘an aggregation of individ-uals who share resources and a common sense of identity,whether or not those individuals actually know oneanother’. These two definitions contain key insights inthe form of similarities and differences that will be used togenerate an alternative working definition of communityfor the purposes of this paper.

As both definitions cited above imply, the context of‘community’ is, in many ways, a conglomeration ofother micro-, meso-, exo- and macrocontexts, includingfamilies, peer networks, the economy and the media, aswell as other aspects of local institutional ‘culture’ suchas religion, education, sport and so forth. The defining of‘community’ can involve recognition of a specific physi-cal space with distinct geographical boundaries (Gephart1997). For instance the individuals, families, sharedresources and shared culture within several neighbor-hood blocks might comprise a community. Similarly, citylimits or census tract boundaries might serve to desig-nate communities. In rural areas, a community’sgeographic boundaries may be drawn even further,including an entire school district or county, forinstance, as shared resources and identity are likely toextend further in such settings. Unlike Darling &Steinberg (1997), the definition invoked here does recog-nize, at minimum, a ‘loose coupling’ of geographic spaceand community. While intracommunity spatial bound-aries may be flexible (i.e. residents may perceive differentboundaries) and, as alluded to above, intercommunityboundaries might be varied in terms of size, evidenceregarding the intertwining of social and geographicspace is too strong to ignore. This nexus has resulted inwhat many sociologists refer to as ‘concentrationeffects’—the concentration in space of people with simi-lar social characteristics (e.g. Wilson 1987, 1996;Massey & Denton 1993).

Regardless of the actual geographic boundaries, it isimportant to stress that it is the individuals, families, peernetworks, schools, churches, youth leagues, etc.—the‘shared resources’ and ‘sense of identity’ emphasized byDarling & Steinberg (1997)—lying within these bound-aries that help create the essence of ‘community’. Thesethings, in aggregate, all form the social dimension of‘community’—the aggregate-level patterns of socialstructure and social process that characterize thegroup(s) within the physical boundaries—that makethem so important in understanding the behavior of indi-viduals within. So, for example, poverty can certainlyexist at the individual or family level of analysis; yetpervasive poverty across a geographically concentrated

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clustering of families can affect the entire collective—even individuals and families from within the collectivewho are not so disadvantaged themselves. Patterns offamily structure (e.g. percent single-parent households)and family process (e.g. parental monitoring) within acommunity say something about the entire aggregate orgroup of families and their

collective

ability to superviseits youth, above and beyond individual- or family-levelsupervision practices. Similarly, there exist group-levelpatterns regarding the way in which youth in a particu-lar area network with other peers; such patterns repre-sent the aggregate-level peer-network structure andculture—characteristics which, once again, speak to theinfluence of the collective on youth behavior, above andbeyond the effect of any one peer network on any oneindividual.

Other influences can be seen at the community level.Individuals are exposed differentially to media influencessuch as television advertising campaigns or depictions ofsmokers in movies. Much of this exposure depends uponindividual factors and choices, including disposableincome available for entertainment media and time spentwatching television; yet, exposure to media influenceexists as a characteristic of a community as well. Adver-tisers make decisions about where to place billboardadvertisements, where to vigorously advertise prizes inexchange for cigarette purchases and so on. While varia-tion in exposure to such messages exists across all theindividuals within close geographic proximity of suchadvertisements, it is still true that as a collective individ-uals comprising this area have a greater exposure ratethan a collective of individuals not within close proximityof such advertisement. Similarly, as a collective, individu-als comprising a community have a community-levelexposure to forces opposing tobacco advertisement andsales. Local ordinances outlawing the sale of tobacco tominors or the advertisement of tobacco products are vari-ables characterizing the community, not individuals;presence of antitobacco organizations, taxes on tobaccoand available health-care services characterize the com-munity context as well (Altman 1990; Syme & Alcalay1982; Biglan 1995).

Religiosity and education are often thought of as indi-vidual-level factors. After all, we can choose our religion,the frequency of our church attendance and the extentand type of education we want to pursue. However, a Jew-ish adolescent growing up in an area that is 95% Catholicwould probably argue vociferously with anyone whodared tell him or her that the religious composition of thecollective was not important—that it had no effect on hisor her life. Religion can be an individual-level character-istic, but it is also clearly part of the shared ‘sense of iden-tity’ that defines community. Similarly, the straight-Amiddle-school student who longs for an Ivy-league

education and dreams of becoming a doctor, but who livesin an area with astoundingly high drop-out rates andwith under-funded and under-staffed public schools, isclearly caught in a struggle between his/her own educa-tional aspirations and achievement and those character-izing the surrounding collective.

It is thus these characteristics of the collectivecomprising a geographically bounded area—the

rates

of poverty,

rates

of single-parent households,

patterns

ofsupervision, the

pattern

of peer networks, the

rates

ofmedia exposure, the religious beliefs and practices of thegroup, the

rates

of school drop-out, the teacher/student

ratio

, etc.—that represent the influence of ‘community’.Blau (1977) referred to these characteristics as ‘socialstructure’, and claimed that social structure accountedfor social differentiation at the aggregate level (e.g. acrosscommunities), which he assumed to be greater thansocial differentiation at the individual level (e.g. acrossindividuals within communities) (see Sampson &Morenoff 1997 for a recent review).

In sum, resembling an integration of the definitionsprovided by Gephart (1997) and Darling & Steinberg(1997), ‘community’ will be defined here as a geographicspace (although geographic boundaries can be impreciseand variable) in which individuals, their proximal con-texts (e.g. families and peer groups, etc.), and their phys-ical structures (e.g. store, churches, farms, schools,hospitals, playgrounds, businesses, billboards, roads, etc.)are embedded, resulting in a larger, more distal contextthat has aggregate social and cultural characteristicsof its own. This ‘social structure’ comprises the com-munity’s shared resources and sense of identity. Itincludes but is not exclusive to community-level eco-nomic disadvantage, community-level family structure,community-level extent and content of peer networking,community-level religious practices, community-levelop

portunities for and emphasis on educatio

n, community-level opportunities for and emphasis on youth athletics,community-level media exposure and community-levelaccess to commercial outlets, tobacco production (e.g.farms) and health care.

While the social structure is of utmost importance,its effects may depend upon the geographic positioningof the community, thus my hesitancy to dismissgeographic/spatial boundaries as a part of the definitionprovided here. For instance, imagine two middle-classcommunities, one surrounded by equally middle-classcommunities and the other an ‘oasis’ in a sea of disad-vantage, surrounded by extremely poor communities.While I suspect main effects of social structure in bothinstances, the effects might be different, conditioned bythe placement of the social structure in physical space.In other words, social structure and physical spaceinteract. Furthermore, the imprecision and variable


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nature of the spatial boundaries is a necessary compo-nent of the definition because, as alluded to earlier,shared social structure and culture can fall within avariety of different types of physical space. This point isillustrated below.

Figure 1 illustrates the distribution of tobacco farmsacross counties in Kentucky—historically, a major pro-ducer of tobacco in the United States

.

Counties might bean appropriate unit for understanding ‘community’-level adolescent smoking dynamics as they relate toacreage under tobacco cultivation since the effects ofcultivation in terms of tobacco availability, smoking cul-ture, etc. surely extend beyond the farms themselvesand into surrounding areas. On the other hand, what ifthe particular aspect of structure/culture of interest inunderstanding adolescent smoking was not tobaccoacreage but population racial composition? Suppose,more specifically, that I presumed that individual adoles-cent smoking has much to do with the percentageof white/non-white students in the schools of theseadolescents. The effects of the percentage of African-American students might be understood best whenexamining schools as communities. This seems par-ticularly helpful in areas in which students are busedrather than attending ‘neighborhood schools’. In suchinstances—such as in Lousiville, Kentucky, the largestmetropolitan area in this tobacco producing state—schools are not really a part of the immediate surround-ing community (e.g. county) in which they are embed-ded and instead represent a subcommunity in their ownright. Some schools in this county have racial hetero-geneity while others have racial homogeneity, withhomogeneity ranging from predominantly white topredominantly black (see, e.g. Meshew 2000). It is prob-able that the racial dynamics in other schools in thiscounty have little impact on students within any oneparticular school; it is the social structure of their ownschool that is most salient to students in any particularbroader area (e.g. municipality or county). As such,analysis of school racial composition at the city, county,district, or even census tract level would be much lessmeaningful in this instance.

THE APPLICABILITY OF COMMUNITY ECOLOGICAL MODELS TO YOUTH SMOKING TRAJECTORIES: A REVIEW OF THE LITERATURE

This section explores whether there is empirical evidencethat community is an important context when it comes tounderstanding youth smoking in particular. This explo-ration entails a review of studies that examine variabilityin smoking across communities or across important com-ponents of communities that comprise shared resourcesand sense of identity but that are not addressed elsewherein this collection of papers.

In general, there is some evidence that non-randomvariability in substance use occurs across ecological aswell as individual units, although this literature is muchmore scant than that dealing with interindividual differ-ences, and little of it deals with tobacco use specifically.Nonetheless, certain schools, neighborhoods, and com-posite ‘communities’ of various shapes and sizes—evencountries—are known as having high

rates

of drug usewhile other ecological areas are noted for lower rates ofuse. Unlike studies of other forms of adolescent wellness-related behavior, such as early childbearing, poverty, aca-demic achievement and delinquency, the neighborhoodhas not been the unit of analysis primarily used in thesestudies. Rather, many studies have examined cross-school differences in rates of substance use, includingsmoking, to hint at the effects of community. It is withschool-based studies, therefore, that the presentation ofempirical evidence regarding effects of communitybegins.

School effects

As Skager & Fisher (1989, p. 129) point out, ‘Educationalpolicies are ordinarily implemented through schools. Itfollows that relationships between school characteristicsand substance use by students are likely to have moreimplications for research and policy than relationshipsbased on student characteristics alone.’ Empirical studies

Figure 1

An example of counties as ‘com-munity’: acreage under tobacco cultivationper county in Kentucky, 1992. Source: Uni-versity of Kentucky Center for PreventionResearch, April 2000


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of the school–substance use relationship incriminateboth school structure and school culture. For instance,Johnston (1973) linked school-levels of drug use toschool size (enrollment), illustrating that small schoolshave very low rates of use. However, contradictory evi-dence abounds. Other studies have suggested thatsmaller, more rural, predominantly white schools havehigher rates of substance use, while poor, minorityschools have the lowest levels of use (e.g. Skager & Fisher1989; Dent, Sussman & Flay 1993). In other school-levelstudies various measures of school culture appear impor-tant, as schools perceived as uncommitted to learningand those with an obvious ‘drug subculture’ increaseadolescent use (Brook

et al

. 1989; Wilcox Rountree &Clayton 1999). Although Bell, Wechsler & Johnston(1997) studied the effects of college campus (e.g. asopposed to middle or high school) characteristics on mar-ijuana use, they found that, controlling for individual-level risk factors, students attending commuter colleges,where campus cultures were less likely to emerge, are sig-nificantly less likely to use marijuana. The findings of Bell

et al

. also point to the effects of variation in access acrossschools. They showed that students attending collegeswith on-campus pubs were at approximately a 30%increased odds of using marijuana than students attend-ing pub-free campuses.

Regarding smoking specifically there are, again, con-flicting findings regarding the effects of school context.Murray

et al

. (1994) showed that measures of prevalenceof weekly smoking and number of cigarettes smoked perweek vary significantly across schools, with intraschoolcorrelations averaging 0.019 and 0.011, respectively,across over 60 surveys; incidence of weekly smoking didnot vary across schools, on average, across the varioussurveys examined in this study. Other researchers havealso found significant and even larger intraschool corre-lations in initiation of smoking, 30-day smoking preva-lence, and number of cigarettes smoked per week (Murray& Hannan 1990; Norton

et al

. 1996). In their study of 36elementary schools, Ennett

et al

. (1997) found intra-school correlations ranging between 0.01 and 0.05 forlife-time prevalence and current use prevalence, withhigher values associated with the life-time prevalencemeasures. Murray and colleagues (Murray & Hannen1990; Murray

et al

. 1994) suggest that variation in intr-aschool correlation is conditional upon several additionalcharacteristics. The intraschool correlations reportedvaried by year of survey (with those measures takenbetween 1981 and 1984 having unusually high intra-school correlations), grade-level of survey,

1

and month ofmeasurement (with surveys conducted in the Fall yield-ing higher intraschool correlations).

Various important school features have been identifiedas causing such cross-school variation. For instance, in a

study of nearly 3000 7th, 9th and 11th graders inVentura County, California, Newcomb

et al

. (1987) foundsignificant mean differences in cigarette use as well asother substance use (except alcohol use) across schoolscharacterized as traditional middle and/or high schooland ‘continuation schools ‘(e.g. alternative schools). Fur-ther, the individual-level risk factors associated with sub-stance use, including smoking, were exacerbated in thecontinuation schools—these risk factors had an evengreater effect on use within such school contexts. In ear-lier work, Murray, Kiryluk & Swan (1984) showed thatcross-school rates of smoking varied according to factorssuch as sex-ratio of the student body, sex ratio of the fac-ulty, curriculum (e.g. presence of health classes, smokingeducation) and school-uniform policies. Also delineatingthe importance of school-level policy, Porter (1982) com-pared two public schools and found smoking rates to behigher in the school that had no student antismoking pol-icy. A much larger study by Pentz

et al

. (1989a) supportsthe power of school policy as well. They also found thatsuch school smoking-related policies affected studentsmoking significantly, especially when enforced in combi-nation with prevention programming. Finally, Ennett

et al

. (1997) found life-time cigarette use to be higher inschools with more prodrug-use attitudes (e.g.a drug sub-culture), a greater perceived acceptability of cigarettes,higher cigarette availability and lower overall schoolattachment among students.

Yet other studies have found intraschool correlationsregarding current smoking status not differ significantlyfrom zero, suggesting no random or non-random varia-tion across schools in such behaviors (Siddiqui

et al

.1996). Siddiqui

et al

. (1996), however, did find significantintraschool correlations for other smoking-related out-come measures, including overall student smokingprevalence and tobacco knowledge. Further, intraschoolcorrelations tended to be higher among the white stu-dents in the sample and they tended to decrease over time(from wave 1 to wave 4) for all students.

Again, because schools are part of the sharedresources and sense of identity that define communities,the empirical evidence regarding cross-school varia-tion in smoking is important for understanding cross-community variation in smoking. Religion and its variouschurches are similarly important components of commu-nity. Thus, understanding variation in smoking across

1

Murray & Hannen (1990) report that intraschool correlationsare higher among 7th, 8th and 9th graders than among 10th

-

12th graders for smoking measures. Murray

et al

. (1994), onthe other hand, find that 11th

-

12th grade status is related to anincrease in the intraschool correlation for smoking behavior. Itshould be noted that the Murray

et al

. (1994) work combinesand summarizes results from many more surveys than does theMurray & Hannen (1990) research.


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religious communities is integral to understandingcommunity-level influence more generally. Evidenceregarding such variation is reviewed below.

Church effects

Although individual-level religiosity in terms of fre-quency of church attendance and/or strength of religiousbeliefs is often found to reduce the risk of substance use(e.g. Burkett & White 1974; Elifson, Petersen & Hadaway1983; Hadaway

et al.

1984; Sloane & Potvin 1986;Cochran & Akers 1989; Cochran 1991; Perkins 1991;DeFronzo & Pawlak 1993; Mullen & Francis 1995;Kendler

et al

. 1997; Maes

et al

. 1999),

2

few studiesapproach the religion–substance use link from an ecolog-ical perspective. However, research conducted on theeffects of denomination on substance use, particularly if itcontrols for individual religiosity, clearly has ecologicalimplications because it implies that it is something aboutthe religion or church itself—an important aspect of com-munity culture—that is affecting use above and beyondthe effects of individual-level church attendance and/orother dimensions of religiosity.

It is commonly recognized that interpretation ofhealth versus illness and the defining of health-relatedbehaviors among subpopulations is intimately inter-twined with their community’s moral/religious beliefs.For instance, conservative and fundamentalist religiousgroups have, at various points in our nation’s history,defined AIDS, drunk driving, alcohol use more generally,marijuana use, opiate use, etc. as ‘immoral’ and thussubject to arguably rather draconian control measures(see, e.g, Brandt & Rozin 1997; Courtwright 1997;Gusfield 1997). Relatedly, research has also shown com-munity-level religious influences on the health behaviorsof community members. For instance, dating back to thework of Durkheim 1895/1938, 1897/1951), macro-sociologists have been interested in the effect ofaggregate-level religious affiliation on rates of problembehaviors. Durkheim presented data showing thatProtestant European countries experienced higher ratesof suicide among their population in comparison withCatholic European societies (see also Halbwachs 1930;Dublin 1933). More contemporary work continues to

show non-random variation in suicide behavior acrossreligious communities (Kramer

et al

. 1972), althoughthe finding emerges less consistently, and aggregate reli-gious affiliation is increasingly thought to be conditionalupon individual-level religiosity (e.g. Epps 1957; Neal1981; van Poppel & Day 1996).

Beyond suicide, scholars have studied the effects ofreligious affiliation on other health-related behaviors,including overall mortality. Cross-group comparisonshave revealed above-average life expectancies amongSeventh-Day Adventists and Latter-day Saints. Lower-rates of cancer related deaths among these two groups aswell as among Hutterites may be related to community-wide proscriptions against smoking (see Jarvis &Northcott 1987 for a review). In addition to examiningthe effects of affiliation, researchers have noted the effectsof size of religious organization on health-related out-comes. For instance, Jarvis & Northcott (1987) suggestthat non-fatal self-injury is lower among members ofsmall religious groups.

In terms of substance use more specifically, researchindicates similar non-random variation in use across reli-gious communities. Religious groups that have strongproscriptions against tobacco have lower rates of cancer-related deaths as well as respiratory-related deaths (Jarvis& Northcott 1987). Moreover, Mullen and colleagueshave found rates of smoking, drinking and other drug useto be higher among Catholics and individuals with ‘noreligion’ than among Protestants (e.g. Mullen

et al.

1986;Mullen

et al.

1996; Engs & Mullen 1999). Cochran

et al

.(1988) suggest that there are variations within Protes-tant denominations as well, with higher rates of useamong Lutherans and Presbyterians and lower rates ofuse among Baptists and Protestant sects. However, itshould be noted that research reveals interdenomina-tional differences in attitudes towards use (in modera-tion) only; cross-affiliation differences tend to disappearwhen misuse of substances is considered, as there arewidespread societal proscriptions against such behavior(Mullen

et al

. 1986; Cochran

et al.

1988).While studies often show cross-denomination differ-

ences in substance use, fewer studies have found thiseffect to hold while controlling for individual-level religi-osity. The work of Francis (1997) represents an excep-tion; holding personal religiosity constant, members ofProtestant churches exhibited less tolerance of a varietyof substances than did Anglicans, Roman Catholics ormainline Protestants. Further, Cochran

et al

. (1988)found that religious affiliation interacted with individual-level religiosity in predicting alcohol use. The effects ofvarious indicators of individual-level religiosity (e.g.attendance, membership, belief in afterlife, etc.) on alco-hol use were most strongly negative among individualswithin more conservative denominations; the effects

2

The literature is unclear as to which dimension of personal reli-giosity (e.g. church attendance, belief in God, importance of reli-gion, etc.) is most important in predicting substance use. In fact,some research shows that the effects of these various dimen-sions may vary at different stages of substance use trajectories.The findings of Kendler

et al

. (1997, p. 327), for example, sug-gest that ‘Traditional religious beliefs may be most important inthe decision to ever use a substance, but religious devotion mayparticularly influence the ability to quit or maintain low levels ofuse’.


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were largely non-significant among members of ‘non-proscriptive’ faiths.

In sum, to the extent that religion characterizes com-munities as well as individuals, cross-denominational orcross-church differences in substance use clearly haveimplications for cross-community differences in use. Thesame can be said for cross-school differences. In addition,variation across other components of community includ-ing families, peer groups, media and economic factors,have implications for cross-community variability. Assuch, cross-community variability itself is a phenomenondifficult to isolate independent of the effects of its con-stituent parts: families, peer groups, schools, churches,media and access provided by farms, stores, and so on.Furthermore, individuals within communities are ofteninvolved in many of these diverse constituent parts, andthese various social networks—all a part of the samecommunity—may have very different effects on the samebehavior. Thus, determining a ‘net effect’of communitycan be complicated. Thompson (1995, p. 88–89) pro-vides indirect evidence of this complexity of community-level context:

Those who are involved in social networks, whether they be informal friendship groups or more formal organizations such as clubs or churches, may be able to call upon others more readily for emotional and instrumental help. Such help may serve as a ‘buffer’ against the stresses of life . . . However, the effect of social integration on particular health-related behav-iors, such as smoking, is largely dependent upon what sort of network one is integrated into . . . peer pressure to initiate smoking may be strong in some groups, nonexistent in others; integration into a church group may result in more social control than integration into a social club. Similarly, changing risky behavior would be more difficult for a person integrated into a network where this behavior is common, than otherwise.

Nonetheless, as mentioned earlier, communities, aswhole entities, do have structural and cultural character-istics that are distinct from the characteristics of theindividuals, families, peer groups, schools, etc. compris-ing the community. It is these community-level effectsthat are addressed below.

Community effects

Despite the difficulty in isolating community effects onsubstance use, some studies have attempted to do so. Forinstance, Dembo and colleagues have examined theeffects of various community factors on alcohol and mar-ijuana use. These studies have suggested that it might bethe

individual

perceptions of the environment as much asthe environment itself that affects drug use. They found

that drug users perceive their environment to be riskier(i.e. greater availability of drugs, a ‘tough’ neighborhood)than do non-users (Blount & Dembo 1984; Dembo

et al

.1986; see also Yarnold & Patterson 1998; Yarnold1998). Dembo

et al

. (1979), however, suggest that per-ceived neighborhood availability of drugs is related indi-rectly to personal use; it operates through other variables,including peer use of drugs. Nurco

et al

. (1996) examinedthe relationships among perceptions of neighborhooddeviance, perceptions of neighborhood dangerousnessand narcotic addiction. They found that narcotic addictsperceived their neighborhoods to be more deviant thandid nonaddicted control subjects.

In his study of opiate use in Chicago, Dai (1970)found that rates of use were highest in communities withpoor housing stock, high rates of family disruption, pop-ulation turnover, low socio-economic status and highrates of delinquency. Esbensen & Huizinga’s (1990) anal-ysis of the Denver Youth Study recognized that neigh-borhoods defined as ‘socially disorganized’ or‘disadvantaged’ could take on qualitatively differentcharacteristics. Despite these differences, there were fewdifferences in prevalence or frequency of drug use acrossvarious ‘disorganized’ communities. However, the rea-sons for use did vary by ‘disorganized’ community, sug-gesting that community-level mediational processes areimportant. For instance, marijuana use in disorganizedand predominantly black neighborhoods was more likelyto be undertaken in order to be accepted or popular withfriends, whereas in more racially heterogenous disorga-nized areas, ‘getting high’ was the most widely cited rea-son for marijuana use. Finally, McBride & McCoy (1981)compared the ecological distribution of specific types ofdrug use—including narcotic use—with the ecologicaldistribution of criminal behavior. They found very similardistributions across Miami census tracts for narcotics useand property crime, suggesting that these phenomenaare located in the same neighborhoods. However,McBride & McCoy found much less evidence of correlateddrug use and crime distributions when considering otherdrugs, including marijuana, sedatives, amphetaminesand other stimulants. While the authors conclude thatnarcotics use and crime may be produced by commonenvironmental variables, the same cannot be said forcrime and use of other types of drugs. This studyhas seemingly important implications for theoreticalapproaches to understanding the ecological distributionof non-narcotic substances, including tobacco. This issueis taken up in the next section.

The effect of community on tobacco use specificallyhas also been examined, although somewhat sparingly.Most of these studies have been evaluations of commu-nity-based field experiments. As scholars have suspectedincreasingly the presence of multiple and embedded con-


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textual influences on adolescent smoking (e.g. see Biglan1995), community interventions have become viewed asa more comprehensive approach to intervention andprevention. Most of the experiments do indeed revealpromising results. For instance, Feigherty, Altman &Shaffer 1991) reported on a community-based interven-tion in four California communities. The communitieswere educated about state law regarding the sale oftobacco to minors, followed by police ‘sting’ operations.Substantial declines in the percentages of stores sellingto minors were seen across the four communities, espe-cially after the police enforcement component (thestings) was introduced. Altman

et al

. (1999) conducteda quasi-replication experiment in four rural Californiacommunities. Significant declines in proportions ofstores selling to minors occurred after implementation ofthe interventions (e.g. community and merchant educa-tion, voluntary policy change). Declines in actual ratesof tobacco use occurred for younger adolescents, but notfor older adolescents. Female adolescents also seemedmore susceptible to the intervention than did maleadolescents.

Forster

et al

. (1998) reported on a similar community-based intervention experiment conducted across 14 Min-nesota communities, although the findings seem lessinspiring than those found in the California experiments.In this particular study adolescent smoking did notdecline in experimental communities, but it increased toa lesser extent than in control communities. Adolescenttobacco purchases declined in communities receivingintervention (i.e. mobilization and community action tochange ordinances, merchant practices and enforcementof policies), but the effect was not significantly differentfrom that in control communities.

Biglan and colleagues have reported success withseveral components of their Project SixTeen—a multiple-module, community-based, tobacco-reduction experi-ment in eight pairs of Oregon communities. Biglan

et al

.(1996a) report that the community effort in the form ofmobilization of positive reinforcement for store clerksrefusing to sell tobacco for minors led to reductions in thecommunity-level proportion of stores willing to sell tominors (e.g. not asked for ID, not ask age of purchaser).The mobilization of positive reinforcements was a multi-pronged strategy including visible signs of communitysupport/endorsement for refusal to sell to minors (e.g. asigned proclomation), merchant education, materialrewards to cooperating clerks, positive publicity in casesof refusal to sell and reminder visits and feedback onprogress to outlets. In another report, Biglan

et al

.(1996b) shared the results of interventions aimed atmobilizing peer and parental antismoking influences.Peer mobilization was accomplished through implemen-tation of peer quizzes, sidewalk art, poster and T-shirt

giveaways, while parental influence was garneredthrough dissemination of pamphlets to parents andstudent–parent quizzes. Results of the experimentshowed that such interventions led to greater exposure toantitobacco messages, increases in knowledge abouttobacco and negative attitudes about use and decreases inintentions to smoke. Similarly, Pentz

et al

. (1989b) andJohnson

et al

. (1990) reported that intervention aimed atcombining school-based programs with local media cam-paigns and programs targeting parents and communityleaders—all part of the Midwestern Prevention Project—affected tobacco and other substance use. Furthermore,these effects were greater than those seen in interven-tions aimed at media and/or parents/community leadersalone.

The message that stems from most of these commu-nity-based intervention studies is that broader commu-nity-level influences are important. Interventions aimedat these influences typically have larger effects than doschool-based interventions alone, for instance. Despitethese promising results, evidence presented by LaPrelle,Bauman & Koch (1992) warns that community-basedfield experiments may in fact under-estimate the effectsof intervention, as high intercommunity variation canmask the treatment effects (see also Murray

et al

. 1994;Norton

et al

. 1996; Siddiqui

et al

. 1996). Their study ofthe effect of media campaigns in 10 regionally andsocio-demographically similar SMSA-sized communi-ties revealed no significant effects. They argue that thisis largely because of insufficient statistical power result-ing from small sample sizes and intercommunity vari-ance in adolescent smoking rates. Furthermore, theseintercommunity differences persisted even after control-ling for sociodemographic variables and personalitycharacteristics. While the analysis of LaPrelle

et al

.(1992, p. 125) suggests that finding community effectsmay be difficult, the implication is that cross-community differences do persist because ‘…there arenatural differences between communities’. LaPrelle

et al

.(1992) continue:

There is no obvious reason why communities should differ much in rates of smoking, although we would expect differences between smaller aggregations such as peer groups, neighborhood, and school . . . Our samples should be large enough to include enough peer groups, neighborhoods, and schools within each city to minimize those differences. But it seems that rates of reported adolescent smoking, particularly experimentation, do seem to show differences from community to community.

Thus, while LaPrelle

et al

. cannot explain the cross-community variation they note its existence, evenwhen individual, peer-group, neighborhood and


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school-level differences are presumed to be adequatelycontrolled.

Other evidence of community-level effects on smokingexist outside community-based intervention studies. Forinstance, Brook, Nomura & Cohen (1989) examinedeffects on adolescent drug involvement (including but notexclusive to smoking) across the contextual domains offamily, peers, school and neighborhood. They found thatneighborhood physical disorder and neighborhood socialcohesion had modest effects on use, but that the effectswere largely indirect, operating through more proximalfamily, peer and school contexts. The effects of schoolvariables, in turn, were mediated by peer characteristics.Perhaps most importantly, Brook

et al

. (1989) found that‘organized’ schools and ‘achievement-oriented’ schoolstempered the effects of peer cigarette use and peer use ofother drugs on adolescent’s own use. The study of ciga-rette, alcohol and marijuana use across 36 schools byEnnett

et al

. (1997) also examined both school-level influ-ences (discussed above) and neighborhood influences.They found that some of the variation in life-time ciga-rette use across schools was accounted for by neighbor-hood characteristics. More specifically, the found thatsmoking rates were higher in schools located in ‘ . . . lowdensity neighborhoods where parents reported greaterneighborhood attachment’ (p. 65). Most recently, datafrom the Project on Human Development in ChicagoNeighborhoods suggest that community-level percentageof black residents lowers the hazard rate of adolescent cig-arette use initiation; in fact, community racial composi-tion accounted for almost half of the difference betweenblack and white adolescents regarding age of initiation(Reardon, Brennan & Buka 2001).

Summary

Although limited in volume, the literature on commu-nity-level and intracommunity institutional-level varia-tion in smoking, overall, suggests the potential futility inconcentrating solely on individual-level predictors ofdrug use. However, in terms of what we really know aboutcommunity influence on adolescent smoking trajectories,these contextual studies are limited in several importantways, as follows.

Extant studies tend to use age-restricted samples

The school-based studies, for instance, tend to use cross-sections of various middle and high-school students. Assuch, we know something about how schools may impact7th graders versus 9th graders versus 11th graders, butbased upon such analyses we cannot understand howschools affect intraindividual change, or the pattern ofaccumulation of smoking transitions or non-transitions

that comprise individual trajectories. The same problemholds for church- and neighborhood-based studies,although these are often conducted on cross-sections ofadults rather than adolescents, thus further limiting ourunderstanding of community influence on adolescentsmoking trajectories.

Some exceptions to the cross-sectional trend in the lit-erature are some of the community-based antitobaccoexperiments (e.g. Biglan

et al

. 1996b; Altman

et al

.1999). For instance, students and parents in Biglan

et al

.’s Project SixTeen were interviewed every 7 weeksover a 10-month period. While this methodology allowsus to see important intraindividual variation (and com-munity effects thereon) within a short time span, theoverall 10-month period is far too short to understandadolescent trajectories comprehensively. Of course, analternative strategy of measuring use and its predictors,for instance once every year for a span of 10 years, wouldleave us with the opposite problem—the inability to pickup changes within the testing intervals. Studies are rarelyfunded for such lengthy periods of time. Thus, studies thatcan calculate changes in cigarette smoking over a year ormore in time (e.g. Murray

et al

. 1984; Norton

et al

. 1996)typically examine two points in time only, leaving largegaps in between measurements and many points in timestill unmeasured. The optimal solution is to take multiplemeasurements per year over a span of many years; this isvery time-consuming and expensive and has thereforenot been conducted to date.

Studies employ different yet overly simplistic measures of tobacco use, impeding comparability and clouding intraindividual changes in patterns of use

Studies examining the effects of community on tobaccouse usually assess either life-time or ‘current use’ of ciga-rettes, relying upon respondent self-reported behaviors.However, measures of current use vary substantially,including ‘any tobacco used over the past year’ (Engs &Mullen 1999), ‘any smoking in the past 6 months’(Dembo

et al

. 1979), ‘any smoking in the last month’(Murray & Hannan 1990; Biglan

et al

. 1996b; Ennett

et al

. 1997) ‘smoking at least once per week’ (Biglan

et al

.1996b; Murray

et al

. 1984; LaPrelle

et al

. 1992), ‘smok-ing in the past day’ (Biglan

et al

. 1996b; Forster

et al

.1998), ‘currently smoking at all’ (Thompson 1995) aswell as frequency measures including ‘number of ciga-rettes smoked per week’ (Murray & Hannan 1990) and‘number of cigarettes smoked per day’ (Mullen

et al

.1986). Thus, it is difficult to discern whether contradic-tory findings are ‘true’ or artifacts of non-comparablemeasures instead.

A few studies use measures that combine self-reportedfrequency and quantity assessments, often tapping ciga-


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Pamela Wilcox

rette use and use of other drugs, resulting in a scale mea-suring extent of polydrug involvement (Skager & Fisher1989; Newcomb & Felix-Ortiz 1992). While these scalesare helpful in estimating ‘degrees’ of smoking and per-haps even addiction, they are used largely within the con-text of substance use studies rather than studies ofsmoking specifically.

Perhaps most importantly, few studies examine morethan two of these various measures in a longitudinaldesign, so intraindividual changes in smoking behaviorcannot be captured precisely. For instance, movementfrom monthly to daily to weekly cannot be captured evenin longitudinal studies if monthly prevalence rates onlyare measured. Further, most studies ignore differences in‘taking a puff ’ versus ‘smoking a cigarette’ (see LaPrelle

et al

. 1992 for an exception) and few studies examinequitting (see Bener & Al-Ketbi 1999 for an exception), sovariation in smoking behavior at the initiation and ces-sation ends of the spectrum is rarely assessed. In short,we suspect that community influences affect currentsmoking, but we have little idea how they affect init-iation, change in frequency/amount, stabilization orquitting.

Validity of such measures is also an issue, as it clearlyhas a bearing on our state of knowledge regarding ado-lescent trajectories of use. Self-reports are subject to biasin the form of both over- and underestimation. However,very few studies reviewed here were able to comparesuch self-reports with other, more objective measures.LaPrelle

et al

. (1992) represent an exception, obtainingbreath and saliva samples along with self-reports oftobacco use.

Group-level studies are often based on very small sample sizes

When examining the influence of community, manystudies still use individuals as the unit of analysis. Thisis problematic, since individuals are clustered non-randomly into communities, and thus failure to partitionthe variance across individual and community levels isinappropriate. Such a strategy is likely to result in anunderestimation of community-level effects. Otherstudies examining the influence of community areaggregate-level in design, with community as the unit ofanalysis. However, this approach is limited in that,typically, only a few communities can be studied all atonce. The community-based intervention experimentsreviewed above, for instance, were conducted on four(Feigherty

et al

. 1991; Biglan

et al

. 1996a; Altman

et al

.1999), six (Biglan

et al

. 1996b), and 14 (Forster

et al

.1998) communities. Limited power reduces the likeli-hood of discovering group-level effects (e.g. see LaPrelle

et al

. 1992).

Extant research largely fails to measure adequately the effects of context net of individual-level effects

Related to the discussion above, few studies have beenmulti-level in the sense of explicitly recognizing and mod-eling the non-random clustering of individuals in com-munity contexts. Studies tend to be either individual-level(with community-level predictors) or community-level(without individual-level predictors). Both approachesmisspecify the partitioning of variance across the nestedlevels, and thus do not estimate effects at one level net ofeffects at other important levels.

While conceptually complex, there are statistical pro-cedures available for integrating and modeling such mul-tilevel effects when examining smoking and its individualtrajectories. Hierarchical modeling procedures provide astatistically sound way of integrating micro- and macro-level variables by incorporating submodels for each levelinvolved in the structure of data (Wong & Mason 1985;Bryk & Raudenbush 1992; Reardon

et al

. 2001). Assuch, they provide an ideal option for the estimation ofcommunity influence on smoking. While hierarchicalanalyses have yet to have a broad impact within the druguse literature, there is a need to invoke such proceduresin lieu of conventional regression analysis when incorpo-rating variables from nested levels. Error structures inconventional regression analyses of drug use, includingsmoking, are modeled such that the estimation of out-comes for individuals within the same social groups areassumed to be unrelated; yet it is simply unrealistic toignore group membership and neglect the dependenterror structure. In fact, doing so (i.e. using single-levelregression models) is posited to cause inefficient esti-mates and actual Type I error rates that greatly exceedthe nominal rate (Hox & Kreft 1994). Given the implicitnested structure involved in much adolescent drug usedata—with individuals grouped within schools and/orneighborhoods, and schools and neighborhoods clus-tered within districts, counties or other social units—ahierarchical design with enough individuals embeddedwithin enough group-level contexts is overdue in thisresearch area (see Duncan, Connell & Klebanov 1997;Blakely & Woodward 2000).

COMMUNITY INFLUENCE: THEORETICAL RATIONALE

There are clear limitations to the community-level workon smoking and, as a result, we actually know very littleabout the influence of community on adolescent smokingtrajectories. Nonetheless, evidence does suggest thatcommunity influences are probably operating. Very fewof the studies reviewed in the preceding section were


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theoretically driven. As a result, these studies themselvesprovide little rationale for understanding the communityeffects, even when revealed. Why and how shouldadolescent smoking trajectories be affected bycommunity influences?

Bronfenbrenner’s model of embedded context is rele-vant in addressing this question. In brief, Bronfenbrenner(1979; also see Aber

et al

. 1997 for a recent review) sug-gested that there are four types of embedded contextsaffecting human development: microsystems, mesosys-tems, exosystems and macrosystems. The first of these—microsystems—are very proximal contexts, includingfamily, peer groups and teachers. An individual has directcontact with microsystems. Mesosystems touch an indi-vidual more indirectly. They are contexts that are at least‘once-removed’ from a microsystem member; so friends ofthe family or parents of one’s peers might partially com-prise the mesosystem. Exosystems are larger and moreorganizationally or institutionally defined. They includeschool systems, civic organizations and local churches,for instance. Macrosystems are the more intangible val-ues and beliefs of a community as well as its socioculturalpractices; macrosystems resemble closely the ‘cultural’context of human development.

Recalling the definition of ‘community’ offered earlier,it should be clear that community consists of microsys-tems, mesosystems, exosystems

and

macrosystems. It isthe totality of these embedded contexts. As such, thecommunity effects occur in a number of different ways, asarticulated by Aber

et al

. (1997, p. 47):

Neighborhoods could have an effect on family pro-cesses and individual development at any or all of these four embedded levels. To the extent that children and youth come into direct contact with neighbor-hoods (unmediated by family or institutions), neigh-borhoods are a microsystem, and their direct effects should be estimated. To the extent that neighborhoods influence the type of employment available to chil-dren’s parents or affect the quality of children’s friend-ships through effects on their freinds’ parents, neighborhoods are mesosystems, and their indirect effects should be estimated. As clusters of linked orga-nizations (exosystems) and macrosystems (networks of beliefs and values), neighborhoods have moderating effects that should be estimated.

How do these interconnected systems, comprising com-munity, affect behavior? Jencks & Mayer (1990) reviewseveral approaches for understanding the mechanisms bywhich community factors may affect behavior (seeLeventhal & Brooks-Gunn 2000 for a recent review).Three of these mechanisms, including (1) collectivesocialization models, (2) institutional models and (3) epi-demic models will be discussed, in turn, below.

Collective socialization models

Models of collective socialization explain communityeffects by suggesting that certain behaviors are pro-moted through the articulation of norms and the infor-mal social control thereof (through supervision andintervention) by community adults—a process oftenreferred to as ‘social organization.’ Collective socializa-tion models stem from the work of Chicago-school soci-ologists in the beginning of the twentieth century(Shaw & McKay 1942). Shaw & McKay found that juve-nile delinquency rates were differentially distributedacross the city in a non-random fashion. They positedthat structural factors of poverty, ethnic heterogeneityand residential mobility created social

dis

organizationand rendered certain neighborhoods incapable of pro-viding adequate control over their residents. Such com-munities could not establish or maintain consensusconcerning values, norms or roles, resulting in a neigh-borhood lacking in informal defenses against crime(Kornhauser 1978). Shaw & McKay (1942) never actu-ally measured social (dis)organization, but theyassumed that the relationships between social structure(heterogeneity, mobility, poverty) and social problemssuch as crime rates resulted from the unmeasuredlatent construct. While much of the contemporary workin the collective socialization tradition over the past sev-eral decades has also left these processes unmeasuredand simply assumed the link between structural charac-teristics and behavior was due to informal social con-trol, there is a growing body of empirical evidenceregarding the theoretically specified mediating role ofindicators of neighborhood ‘collective socialization.’More specifically, contemporary scholars have revisitedShaw & McKay’s (1942) theoretical suggestionand have found empirical evidence that structuralcharacteristics of communities do seem to affectneighborhood-level indicators of informal controlwhich, in turn, affect rates of various indicators of childand adolescent wellbeing, including infant mortality,crime and delinquency, school achievement, sexualbehavior and physical abuse (Garbarino 1976;Garbarino & Crouter 1978; Garbarino & Sherman1980; Lash

et al.

1980; Sampson & Groves 1989;Wallace & Wallace 1990; Wallace 1990; Felner

et al

.1991; Brooks-Gunn

et al

. 1993; Bursik & Grasmick1993; Furstenberg 1993; Steinberg & Darling 1994;Coulton

et al

. 1995; Furstenberg & Hughes 1995;Warner & Wilcox Rountree 1997; Dahlberg 1998;McLoyd 1998; Furstenberg

et al

. 1999; see also Gephart1997 for a review). Some of the most recent ecologicalmodels of social control have focused specifically onthe role of ‘collective efficacy’ (the aggregate-levelprocess of putting social capital into effective motion)


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mediating between neighborhood structure and prob-lematic outcomes (Sampson

et al.

1997; Sampson,Morenoff & Earls 1999).

This ‘informal social control’ or ‘collective efficacy’that lies at the heart of collective socialization modelsincludes not only the processes of supervision andintervention—presumably somewhat linked to adultfriendship and kinship ties and neighbor networks—but also the process of attenuation of conventionalnorms. Structural disadvantage can create not only inef-fective supervision and intervention, but also an attenu-ation of mainstream values. Ethnographic evidence, inparticular, supports the idea that people in particularlydisadvantaged and isolated communities may perceivemiddle-class expectations as unviable given structuralconstraints (Suttles 1968; Anderson 1990; Dash 1996;Wilson 1996; Furstenberg

et al

. 1999). Exacerbating thisattenuation is often an absence of positive adult role mod-els (Jencks & Mayer 1990). Under such situational condi-tions, adaptive—often deviant—strategies are employedas techniques for ‘getting by’. Sampson (1992, 1997)describes this process in suggesting that communitiesstructure helps form ‘cognitive landscapes’ for formationof normative expectations within the specific local set-ting. In settings in which following mainstream codes ofconduct seems untenable, ‘deviant’ behavior, includingadolescent drug use, teen pregnancy, and violence, canbecome accepted as a way of life and thus easily culturallytransmitted among members within a community(see also Sucoff & Upchurch 1998; Warner & WilcoxRountree 2000). The idea of ‘cognitive landscapes’ does

not

intend to suggest that individuals who engage in‘deviant’ behaviors actually value such behaviors.Rather, constrained individuals realize that attaining‘conventional’ values may be impossible and thus theirvalues and norms become ‘weakened’ to the extent thatalternatives are accepted although not valued. It isimportant to note that this process is subtly yet impor-tantly different from the processes of differential associa-tion or social learning—processes that lie at the heart ofcontagion models instead.

Contagion models

Contagion models suggest that people within a commu-nity adopt similar patterns of behavior, primarily throughpeer influence. Contagion models are rooted in differentialassociation (Sutherland 1939) and social learning theo-ries (Akers 1977; Bandura 1977), which imply thatbehavior often stems from an adherence to underlyingvalues. These perspectives recognize value conflict in soci-ety as opposed to normative consensus (the assumptionunderlying collective socialization models). Conventionalversus non-conventional attitudes and behavior are

adopted through processes of socialization and cognition,modeling or even peer pressure, and involve interactionwith ‘conventional’ versus ‘deviant’ peers.

The effect of peer associations on adolescent behavioris one of the strongest (Kandel 1978; Kornhauser 1978)yet most contested relationships in social psychology.Scholars studying human development from a contagionperspective stress that delinquent peer associations fosterthe transmission of definitions favorable to violation oflaw (Sutherl and 1939) and reinforcements (Burgess& Akers 1966). Evidence in support of the effects ofsmoking–peer associations, pro-smoking definitions, andpro-smoking social and non-social reinforcements onadolescent smoking has been, relatively speaking, abun-dant (e.g. Chassin

et al

. 1981; Ureberg & Robbins 1981;Krosnick & Judd 1982; Rooney & Wright 1982; Flay

et al.1983; Krohn et al. 1986; Burton et al. 1989; Sussmanet al. 1990; Ureberg et al. 1990, 1991, 1997; Ennett &Bauman 1993; Distefan et al. 1998; Wang et al. 1999;see Kobus 2003 for a thorough review).

Institutional models

Institutional models suggest that the organizationaleffectiveness of institutions within neighborhoods greatlyaffects the rates of risky/harmful behavior within suchecological areas (Jencks & Mayer 1990; Duncan &Raudenbush 1999). In many ways, institutional modelsrepresent the intersection of collective socialization andcontagion models. This nexus seems to represent variousdimensions of ‘opportunity’ (e.g. opportunity to smoke oropportunity for alternatives to smoking). Communityinstitutional resources—in the form of schools, churches,recreation, libraries, hosptials, etc.—are important interms of providing supervision and intervention (e.g. col-lective socialization) as well as ‘healthy’ learning (e.g.contagion models). As such they really provide opportu-nity structures in which behavior can and will probablyoccur.

In terms of the monitoring aspect of institutionalresources models, many community-level scholars viewthe presence of intracommunity institutions as an indica-tor of informal social control—of semistructured supervi-sion of youth and intervention in youth problembehaviors. For instance, in their exposition of the sys-temic model of social disorganization theory, Bursik &Grasmick (1993) suggest that informal community con-trol has three dimensions: private, public and parochial(see also Hunter 1985). Private control refers to thesupervision and intervention that emerges from friend-ship and kinship networks, from informal, primary tiesamong residents (see the discussion of the role of socialties above). Public control refers to the supervision andintervention that emerges from ties to extra-community

Ecological approach to understanding youth smoking trajectories 69

© 2003 Society for the Study of Addiction to Alcohol and Other Drugs Addiction, 98 (Suppl 1), 57–77

agencies. Parochial control refers to the supervision andintervention that stems from less proximate, less intimateassociations among neighbors, such as ties in the form ofneighborhood resources/subinstitutions, for instanceschools, churches and voluntary organizations. Theextent to which a community has such an institutionalinfrastructure and organizational participation amongits residents is thought to increase the likelihood ofinformal social control and thus decrease neighborhoodproblems.

However, it is clear that not all neighborhood subin-stitutions are equally effective in controlling behavior. Inmuch the same way that neighborhood structural char-acteristics can affect levels of informal community-levelsupervision and intervention, the structural features ofthe school can affect the ability of school personnel andstudents to monitor one another effectively which, inturn, helps determine levels of misbehavior. As an exam-ple, the size of the school, the faculty/student ratio andthe physical layout of the school (e.g. does it have spaceswhere students can gather free from the eyes of others?)can all affect the ability of supervision and intervention.Similarly, the resources of the school or the school’s orga-nizational administrative hierarchy can influence themanner in which students are monitored/disciplined andthe effectiveness of this control.

In addition to affecting supervision and interventionpractices, school structure (or any other institutionalstructure for that matter) may affect the culture thatarises, which in turn may affect levels of misbehavior (e.g.through the process of contagion). For instance, theresources a particular school has at its disposal or the levelof experience/tenure among its faculty may affect the ‘ori-entation’ that is promoted within the school and thatpervades both staff and students. Perhaps academics areemphasized; students who excel academically areesteemed not only by the faculty but by their peers as well.On the other hand, perhaps the orientation of the schoolis towards athletics. While academic courses are certainlytaught in all schools, the fervor with which they are deliv-ered and received is variable. In some schools, the rewardsfor excelling academically may not be as obvious as therewards for succeeding athletically. In other schools,neither of these orientations may be obvious; it may bethat students feel they stand to gain the most by beingcompletely disinterested in ‘mainstream’ activities—thatsmoking in the bathroom, skipping school and failingclasses are the way to be ‘in’ within this particular school.Again, these different orientations may be inextricablylinked to various features of the school’s structure.

Which of these three classes of models, if any, seemsmost applicable for understanding the effects ofcommunity on adolescent smoking trajectories? Regard-ing the first set of models—collective socialization

models—very little evidence points to their utility in pre-dicting smoking behavior. Interestingly, collective social-ization models have been applied widely to a variety ofaspects of adolescent development (e.g. see Gephart 1997for a review), yet smoking has not been among these. Thisis not surprising, as the ‘fit’ seems lacking between thetheory and empirical evidence regarding the ecologicaldistribution of rates of smoking. Collective socializationmodels assume that ‘problem behavior’ will be most com-mon in the most ‘disadvantaged’ communities. This prop-osition holds true for behaviors such as delinquency,serious drug use, out-of-wedlock childbearing and aca-demic failure. Rates of non-narcotic drug use and adoles-cent smoking, however, are typically lower in such‘disadvantaged’ communities (e.g, in predominantly poorminority neighborhoods) in comparison to relativelymore advantaged areas (e.g. see McBride & McCoy 1981).

Why does the pattern not hold for smoking? Recallthat collective socialization models are built upon theassumption of underlying value consensus—this consen-sus is necessary if values are to be upheld through effec-tive community-based informal social control (the crux ofthe models). As such, scholar Robert Bursik (1988) seesfew exceptions to the breadth of deviance falling withinthe scope of such models, arguing that inapplicabilitymay only characterize behavior for which there is a lowdegree of community consensus regarding morality orimmorality—for instance, public-order offenses such asgambling—and crimes for which there is no generalthreat involved, such as white-collar crime. Given theillicit nature of adolescent smoking in combination withthe growing public awareness of the dangers of smok-ing—a danger targeted particularly at minors—it isincreasingly likely that there may be growing consensusamong community members about the harmfulness ofadolescent smoking, thus making it a behavior poten-tially susceptible to community-level social control. How-ever, it is also clear that despite any growing consensusregarding opposition to youth smoking, it is still not asstrong as consensus surrounding other behaviors. Manystill view smoking as a behavior indicative of a ‘kids willbe kids’ stage through which many adolescents travel—abehavior without serious immediate consequences. Ifsuch a view exists, then smoking is not highly susceptibleto informal community control and can therefore flour-ish even in the most advantaged of communities.

Contagion models seem less at odds with the evidenceregarding the ecological distribution of adolescent rates ofsmoking. Contagion models do not link rates of smokingto ‘disadvantage’ but to the presence of pro-smokinginfluences within the community, including peer groupsmost importantly. Pro-smoking peer groups can appearin any sort of neighborhood. While the effect of such peergroups on individual smoking behavior has been, as


70 Pamela Wilcox

noted above, studied widely, little research has examinedthe way peer groups affect community-level rates ofsmoking; but such an effect seems possible. Thinkingabout the ways in which religious denominations orchurches have varying rates of smoking is helpful in see-ing how the effect might operate.

Literature on the relationship between religion andmortality suggests that denomination can affect illnessand death in several ways. First, religious doctrineregarding certain behaviors and practices is important(Jarvis & Northcott 1987; Brandt & Rozin 1997). Inother words, the doctrines provide ‘rules’ that becomethe basis of the value systems of believers. The doc-trines provide norms that can help prevent illness/harmif healthy habits are endorsed. On the other hand, suchnorms may increase mortality if the behaviors advo-cated encourage illness/harm. Not only are the behav-iors supported or advocated by religion important, soalso are the behaviors proscribed by religion. If behav-iors proscribed are associated with health risks (e.g.tobacco use, alcohol use), proscription of such behaviorcan positively impact illness/mortality. However, if thebehaviors proscribed are associated with health benefits(e.g. medication, surgery, other professional interven-tions), proscription of such practices can heighten ill-ness/mortality. Extending the ideas expressed in themorality/mortality literature, it is obvious that religiousculture can impact other health-related behaviors—including the use of substances such as tobacco—through the establishment of a normative code thateither permits or proscribes such behaviors. Forinstance, the Seventh-day Adventist and Latter-daySaints groups, described above as having lower thanaverage rates of mortality, proscribe tobacco, alcohol,caffeine, certain meats and sexual promiscuity. Theyencourage forcefully exercise, fasting and weekly ‘daysof rest’. It can be seen easily how such cultural norms,stemming from religious affiliation, can impact health-related behavior.

Pro- or antismoking socialization can clearly comefrom community-based peer groups other than religiousgroups. Adolescent groups help form the culture of acommunity—such as ‘the jocks’ or ‘the greasers’—andcan affect the rates of behavior of the entire community,subparts of the community and individuals. Evidencethat the ‘culture’ regarding drug use within a schoolaffects individual-level drug use (Brook et al. 1989;Ennett et al. 1997; Wilcox Rountree & Clayton 1999)provides partial support for contagion models. Further,evidence from school-programming components ofcommunity-based intervention studies suggest that anti-smoking programs implemented through schools andaimed at resocialization can be useful in affecting atti-tudes of students and parents.

Qualitative research by Lawson (1994) supports sim-ilar ideas regarding the impact of contagion, even withinsmall subgroups within any one entire community. In afield study of 20 low-income pregnant adolescents,Lawson found that cultural ideals dictated the benefits ofsmoking even when risks were known.3 For instance, thewomen in Lawson’s study smoked during pregnancy inorder to decrease the pain and duration of labor, and sev-eral actually saw smoking as a relatively positive adaptiveresponse to stress given others in their environment wereusing ‘harsher’ drugs, prostituting, etc. Clearly, integra-tion into the networks available in the communities ofthese adolescents would not exert antismoking socialcontrol and would, in fact, exert control in favor of smok-ing (see also Dekker 1975; Syme & Alcalay 1982;Andreski & Breslau 1995). Similarly, Smith & McGraw’s(1993) study of smoking among female Puerto-Ricancaretakers support the idea of socio-cultural context pro-viding meaning to gender and ethnic patterns of cigaretteuse. The Puerto-Rican women in this sample with highprevalence rates of smoking rarely finished high schooland the majority were not living with husbands or part-ners, although these effects were somewhat attenuatedby involvement in Catholic or fundamentalist religiousorganizations, ‘that frequently maintain stricturesagainst smoking’ (Smith & McGraw 1993, p. 147).

Finally, institutional-resources models appear helpfulin understanding the influence of community on adoles-cent smoking. Plentiful and effective institutions can pro-vide both antismoking social control and socialization,thus limiting the overall opportunity for smoking.Schools, for instance, provide not only value systems, asmentioned above, but they provide a system of monitor-ing and supervision. In addition to the prescriptions andproscriptions of religious norms, churches can also affecthealth by ‘plac[ing] an individual within a support groupwhich can, depending on the strength of the group, assistin times of need; and cultivat[ing] attitudes which maygive the individual perspective with which to face stress-ful life situations’ (Jarvis & Northcott 1987, p. 813; seealso Idler 1987; Musick 1996). Thus, community reli-gious culture serves to reinforce norms, but churches canalso provide ‘healthy’ alternatives to risky behaviors inthe face of adverse, stressful conditions. Health servicesand antitobacco agencies in a community are also insti-

3Other studies, without the explicit focus on understanding thesocio-cultural context of risk-taking, substance use, etc. corrob-orate such findings in that awareness of risks associated withsubstance use can actually be significantly positively relatedto use (e.g. Yarnold 1998) or, at a minimum, non-significant(Yarnold & Patterson 1998). These studies are in contrast toothers (e.g. Bachman et al. 1990), who suggest that associateddangers/risks are related to lower or declining rates of use.



tutional resources that affect smoking behavior throughboth the teaching of antismoking norms and the socialcontrol of those norms.

Contagion and institutional resources models alsoappear helpful in explaining change in adolescent smok-ing, thus helping understand the various individual tra-jectories of use rather than static use patterns. Contagionmodels imply that normative influences and, ultimately,behavior, will change as the peer groups in which one isimmersed change or as the values of the groups change.Institutional resources models imply that the shape of tra-jectories will be affected by the programs, material andemotional support, etc. offered by the institutional base ofa community in which an individual is embedded. So, ifan individual changes community of residence or if theinstitutional resources available within one communityvary, then smoking status may change, thus affecting theoverall trajectory.

As an example of community influence on individualsmoking trajectories, consider hypothetical trajectoriesduring the transition from middle to high school, asdepicted in Figs 2 and 3. Figure 2 depicts hypotheticaltrajectories of two students, each progressing from middleschool 1 to high school 1 between the 8th and 9th grades.Student 1 appears to have initiated tobacco use before 7th

grade, increased use throughout middle school but thendiminished use in high school. Student 2’s trajectory issimilar, except on a smaller scale—he or she has experi-enced lower levels of use at all points along this abbrevi-ated trajectory. The similarity in the shapes/trends of thetwo trajectories suggests, if all else were held constant,that the context of the high school is effective in curbingtobacco use.

Compare this example with that depicted in Fig. 3.Both students portrayed in Fig. 3 start the 7th grade,again having initiated use, but using at very similar, lowlevels in terms of frequency. While their use increasesvery gradually throughout the middle-school years(again, in middle-school 1), a very noticeable, sharpincrease coincides with the transition into high school2. If all factors were held constant, these trajectorieswould suggest that high school 2 is much less effectivein discouraging use and, in contrast, seems to encour-age use. The institutional perspective implies that highschool 2 lacks sufficient resources for control (e.g,faculty/student ratio, money for antismoking pro-grams) and/or socialization (e.g. norms regarding fac-ulty, student smoking, academic emphasis, etc.) incomparison to high school 1.

MOVING TOWARDS A MULTI-LEVEL, INTEGRATED MODEL OF ADOLESCENT SMOKING TRAJECTORIES

Drawing upon the previous research and community-level theory reviewed above, a multi-level conceptualmodel of community-level effects on smoking is providedin Fig. 4. As Fig. 4 suggests, the effects on smoking comefrom multiple levels of analysis, and these levels canclearly interact. Characteristics that lie between in–dividuals have received the bulk of the attention inthe smoking literature, and they are certainly of greatimportance in explaining smoking (see arrow 1).4 So, anintegrated, multi-level framework does not preclude thefact that there are intracommunity, interindividualdifferences.

Arrows 2 and 3, however, suggest that it is not onlyinterindividual differences that impact individual smok-ing behavior. Rather, intercommunity/institutionalforces impact such behavior as well, and these commu-nity-level forces are broad and varied, representingschool characteristics, neighborhood demographic char-acteristics, religious culture, media influence, economic

Figure 2 Hypothetical tobacco use trajectories, middle school 1 tohigh school 1

0

2

4

6

8

10

12

7th Grade(2001)

8th Grade(2002)

9th Grade(2003)

10th Grade(2004)

Grade (year)

Tob

acco

use

per

mon

th

Student 1

Student 2

Figure 3 Hypothetical tobacco use trajectories, middle school 1 tohigh school 2

05

1015

2025

3035

40

7th Grade(2001)

8th Grade(2002)

9th Grade(2003)

10th Grade(2004)

Grade (year)

Tob

acco

use

per

mon

th

Student 1

Student 2

4While the ‘inter-individual differences’ listed in Fig. 2 areprimarily demographic and social–psychological variables,other important interindividual differences exist, includingneurologically-based differences, personality differences, etc.


72 Pamela Wilcox

context, health services and so on. These characteristicsof communities and intracommunity institutions inwhich individuals find themselves embedded can exertmain effects. In addition, the effects of community can bemediated by community-level processes, including theprocesses of control and socialization discussed here.Also, community-level characteristics may interact inproducing certain tobacco-use outcomes. For instance,the effects of religious culture may be conditional uponother indicators of sociocultural context. The work ofFerraro & Jewell-Patton (1988) demonstrates thismacro–macro interaction. They found that, despite anti-smoking resolutions of the Southern Baptist Convention,religious affiliation (a proxy for religious culture) wasunrelated to smoking behavior and was negativelyrelated to the definition of smoking as ‘deviant’ and advo-cacy of antismoking controls in North Carolina—a stateheavily dependent upon tobacco production. In thisinstance, it appears as if religious culture and the eco-nomic system can interact in affecting smoking-relatedbehaviors.

Perhaps the most important role of interinstitutionaldifferences is the potential they hold for moderating orconditioning the effects of interindividual differences onsmoking (e.g. micro–macro interactions). Such potentialis illustrated by dashed arrows 5 and 6 in Fig. 4. Thesepaths suggest that the effects of interindividual differ-ences are not constant across different institutionalsettings. Instead, the effects of age, gender, parentalsmoking, neurological predisposition, etc. vary according

to the characteristics of the social settings in which suchindividual differences are nested. For example, the influ-ence of parental smoking on an adolescent’s own smok-ing behavior may be conditional upon whether or not thecommunity schools offer an effective antitobacco educa-tion program; or the influence of individual-level anti-smoking media exposure may be moderated by theavailability of tobacco and the integration of tobacco intothe local economy (e.g. as measured by number oftobacco farms, for instance). The quantity and quality ofsuch micro–macro interactions are seemingly endless,but regularities surely exist. One goal of future tobaccoresearch therefore should be to unearth such patterns ofmicro–macro interactions.

To complicate matters, the entire model depicted inFig. 4 is embedded in time. As such, the interindividualcharacteristics, institutional characteristics and theinterplay between the two is dynamic as opposed to static,constantly changing as individuals progress throughoutthe life course. At certain points in the life course, familyinfluences are stronger than peer influences or mediainfluences. School influences may affect initiation but notcontinuation or cessation. Any of these influences mightbe moderated by religious culture at certain points intime, and perhaps by availability of antitobacco agenciesat another point in time. The result of this hypotheticaldynamic contextualism is, of course, an individual’ssmoking trajectory.

As if this potential for time-varying covariates andtime-varying interactions were not complex enough,

Figure 4 Conceptual multi-level model of individual smoking

Community/institutionalcharacteristics

( e.g. neighborhood poverty,neighborhood ethnic

heterogeneity, neighborhood residential stability, number

of hospitals, number of tobacco farms, number of antitobacco agencies, school size, school

governance, school SES, school academic culture,

school curriculum (includingantitobacco education),

religious culture)

Inter-individual differences(e.g. age, gender, race,

income, family attachment,parental smoking, peer smoking,

sibling smoking, religiosity)

Individualsmoking

Community processesSocial control

(supervision, intervention)Contagion

(socialization, modeling)Opportunity

1

2

3

4

5

6



context may have lagged effects, as ‘even at an early age,local communities could be laying the groundworkfor large neighborhood differences that will even-tualy emerge in later adolescence or early adulthood’(Furstenberg et al. 1999, p. 168). There may be ‘crucialcomponents of growth trajectories that vary by neighbor-hood but do not influence the success outcomes exam-ined [here] until the young person is somewhat older’(Furstenberg et al. 1999, p. 168). This idea of dynamiccontextualism, therefore, is useful in explaining apparentparadoxes when it comes to theories of neighborhoodinfluence (e.g. collective socialization models) and empir-ically found ecological patterns of smoking. For instance,as stated earlier, communities with high percentages of‘disadvantaged’ residents actually have lower rates ofadolescent smoking. Such a pattern seems to suggest thatneighborhood context may not affect smoking behavioras it affects crime, illicit drug use or other public healthconcerns. However, when rates of adult smoking areexamined, rates in ‘disadvantaged’ communities exceedthose of ‘advantaged’ adults. So, it may be the case thatneighborhood disadvantage has less to do with whyadvantaged adolescents start smoking and more to dowith why disadvantaged young adults do not stopsmoking.

Ideally, the model shown here would be estimatedwith growth curve analytical techniques. As discussedearlier, it is imperative that we study the smoking from adevelopmental or life-course perspective and discernintra- and interindividual heterogeneity in smokingtrajectories (see, e.g. Reardon et al. 2001). However, inexamining such trajectories, we do not want to abandonthe notion that such individual trajectories are embeddedin various social contexts. Such contexts, along with indi-vidual-level characteristics, may be of great importancein understanding pathways and turning points or transi-tions in any adolescent smoking career. Thus, it is oftenhelpful to employ two-, three- and four-level growthcurve models. Such models are structured similarly to n-level hierarchical models, except that the level 1 modelrepresents the individuals’ developmental trajectories,the level 2 model represents the individual-level charac-teristics that may affect growth trajectories, and level 3,level 4 . . . level n models represent variation in growthparameters across the various (n) contexts in which theindividuals are nested, including schools, churches andneighborhoods (Laird & Ware 1982; Bryk & Raudenbush1992; Goldstein et al. 1994). In essence, these growthcurve models employing hierarchical regression tech-niques represent an integration of the best features of astraightforward two- or three-level contextual modelwith repeated-observations models. In short, change canbe detected, but most importantly it can be understoodwithin appropriate individual- and community-level con-

texts. Such a growth curve approach in which we canestimate not only the rates of change in smoking, but alsoindividual- and contextual-level influences on variationin such rates of change across schools, communities andother institutional contexts epitomizes ‘dynamic contex-tualism’ (see. e.g. Biglan 1995) and, if employed, canchart a new frontier for the understanding and preven-tion of adolescent smoking trajectories.

ACKNOWLEDGEMENTS

This study was prepared for the Tobacco EtiologyResearch Network, funded by the Robert Wood JohnsonFoundation. The author would like to thank Jeff Jones,Michelle Campbell Augustine and Graham C. Ousey fortheir assistance in the construction of some of the figurescontained herein. The author is also grateful for com-ments on previous drafts provided by Richard R. Claytonand Brian Flay.

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An ecological approach to understanding youth smoking trajectories: problems and prospects