The Sociobiologic Integrative Model (SBIM): Enhancing the Integration of Sociobehavioral, Environmental, and Biomolecular Knowledge in Urban Health and Disparities Research

Abstract

Disentangling the myriad determinants of disease, within the context of urban health or health disparities, requires a transdisciplinary approach. Transdisciplinary approaches draw on concepts from multiple scientific disciplines to develop a novel, integrated perspective from which to conduct scientific investigation. Most historic and contemporary conceptual models of health were derived either from the sociobehavioral sciences or the biomolecular sciences. Those models deriving from the sociobehavioral sciences generally lack detail on involved biological mechanisms whereas those derived from the biomolecular sciences largely do not consider socioenvironmental determinants. As such, advances in transdisciplinary characterizations of health in complex systems like the urban environment or health disparities may be impeded. This paper suggests a sociobiologic organizing model that encourages a multilevel, integrative perspective in the study of urban health and health disparities.

INTRODUCTION

Recently, several researchers have hypothesized pathways that attempt to explain how the sociobehavioral environment is related to health and health disparities.^1–9 Historically, these conceptual frameworks have formed a solid foundation upon which science was built. Upon review of these frameworks, it is possible to make at least three general observations. The first is the lack of depth to which they integrate our present understanding of the biology of disease, particularly at the cellular and molecular levels. With the exception of those pathways based on stress (neuroimmunological) mechanisms, the published frameworks in the behavioral sciences and epidemiological literature largely lack clearly stated, causal biologic connections to observed health outcomes.^{2,4,5,7,10,11} On the other hand, the biologically oriented formulations poorly account for socioenvironmental and behavioral effect modifiers that may affect the pathogenesis of disease and the development of health disparities.^12–15

Secondly, the terminology used to characterize the effects of causal agents has not been standardized across disciplines and is derived from toxicology, biostatistics, epidemiology, sociology,¹⁶ and the clinical/bench sciences.^17,18 Across these fields, the terms cause, mediator, moderator, regulator, effector, interaction, and mechanism of action have vastly different meanings, which may not be readily apparent to all investigators. For example, epidemiologists and statisticians tend to use the terms mediators and moderators to describe distinct aspects of an observed association between two independent variables, largely without reference to the underlying biophysiologic processes. On the other hand, toxicologists and clinical/bench scientists tend to use the terms mediators, moderators, and regulators almost synonymously as descriptors of factors, substances, or agents that alter some characteristic of a known or unknown biophysiologic mechanism. They also tend to reserve the term “cause” or “causal pathway” to describe an agent or series of biophysiologic events that must occur to result in a given outcome.

Finally, scientists and investigators trained in the clinical and bench sciences generally consider discreet, quantitative exposures (viral, bacterial, toxicological, psychological, etc.) as the etiologic agents of disease. Historically, these exposures were studied in isolation from the broader sociobehavioral contexts in which they exist. On the other hand, social scientists often consider more qualitative social factors like poverty, socioeconomic status (SES), and racial segregation as the key determinants of health.^19,20 They often assert that other more quantitative exposures are factors, which alter the nature of the association between the social factor and a given health outcome.²¹ When social scientists are describing causal factors, they draw a distinction between proximal social factors, which they define as the settings in which people live (family, work, school, and neighborhood), and distal social factors, which they define as the pervasive forces in society (culture, SES, and race relations).²¹

The unique perspectives of each scientific discipline both have strengths and weaknesses. However, as transdisciplinary investigation is increasingly undertaken, the resultant confusion in scientific discourse may hinder scientific inquiry and the advancement of knowledge.

Given this level of complexity, a sociobiologic organizing model or framework could enhance the nascent link between sociobehavioral investigation and biophysiologic or biomolecular mechanisms. Attempts to organize and understand complex biologic systems were attempted in the past. Some researchers have looked to Chaos Theory and Complexity Theory as constructs to facilitate the understanding about health and its relationship to diverse processes and outcomes such as cardiac arrhythmias^22,23 and even urban epidemics.^24,25 Whereas this approach may have merit, it appears to be beyond the practical usefulness of many clinicians and scientists.

The inherent difficulty of developing a useful transdisciplinary model is demonstrated by the fact that any model detailing all possible biologic pathways through which all possible social and behavioral factors impact all possible health outcomes would be exceedingly complex. Alternatively, an overly simplistic model would likewise be of little value.¹⁹ The goal of this paper is to articulate a framework through which multilevel, transdisciplinary work might be collectively organized.

In this paper, an exhaustive analysis or critique of the science is not attempted. Rather, at each step of our model we will provide illustrative examples that suggest how information from disparate fields might be integrated within a single biologically plausible, mechanistically driven, multilevel framework.

THE SBIM

In brief, the sociobiologic integrative model (SBIM) suggests that individuals are constantly being exposed to many health-impacting environmental inputs. These inputs are often modified to increase or attenuate their effects via other “indirect” environmental inputs. Both direct and indirect inputs are, in turn, acted upon by metabolic, digestive, and/or detoxification systems, often producing measurable biologic products (biomarkers). If inputs or metabolic products overwhelm bodily defense or regulatory mechanisms, disease will occur. Because inputs, biologic processes, and outcomes exist on several levels, the model is conceived as operating on the cellular, individual, and population levels, temporally proceeding from input (exposure) to outcome (Figure 1).

FIGURE 1.

This graphic depicts the SBIM. It shows the general temporal relationship between exposures (direct inputs), factors which can alter these exposures (indirect inputs), biophysiologic mechanisms impacted by these exposures, and potential observed outcomes on the population, individual, and cellular levels.

As applied to our model, the term “environmental” is used in a broad sense. It includes factors such as toxicological agents, microbial pathogens, and sociocultural and geopolitical influences. Because these exposures are extremely varied and emanate from many very different types of sources, we refer to them collectively as inputs. Specifically, we define direct inputs as those exposures that directly alter normal host DNA (directly causal). In contrast to direct inputs, many exposures impact host physiology only indirectly, albeit at times profoundly. Examples of these types of indirectly acting exposures would include culture and SES. We define these indirectly acting factors as indirect environmental inputs.

Population-level direct inputs would include certain carcinogenic emissions from urban factories or diesel exhaust fumes among inner city residents and workers. Individual-level direct inputs would include such things as alcohol- and food-based carcinogen exposures. Finally, cellular direct inputs would include tumor suppressor genes, which confer increased susceptibility of cancer on those who possess the genes.

As with direct inputs, indirect inputs exist on the cellular, individual, and population levels.^{17,19,21,26–30} For example, tobacco regulatory policies may function as population-level indirect inputs that impact cigarette carcinogen exposure. Thus, people who work in smoke-free environments would potentially be exposed to less work-related tobacco carcinogens than those working in smoke-filled workplaces. Individual-level indirect inputs include gender, age, host immune status, health literacy, and family or social networks. Social networks, for example, impact health outcomes because individuals with larger and more robust social networks tend to engage in health-promoting behaviors and to avoid health-damaging behaviors.³¹ Finally, other indirect inputs, such as genetic polymorphisms of tumor-associated genes, operate at the cellular or molecular level, impacting gene function or expression (see “LUNG CANCER AND THE SBIM” discussion below).^{17,32,33,33–39}

The specific group of direct and indirect inputs to which an individual is exposed may be highly variable between individuals or populations. Ultimately, it is determined by the sociocultural milieu or surroundings in which the individual lives, works, and socializes.

After being altered by indirect inputs, all inputs (direct and indirect) are acted upon by one or more degradory, detoxification, immune, or metabolic systems or biologic processes within the body. These systems include, but are not limited to, the N-acetyltransferase enzymes, the phase I cytochrome p450 (CYP) system, and the phase II glutathione-S-transferase (GST) system.³⁴ Although there are potentially many such systems operating in the body, the absolute number is finite. Integratively understanding health requires that the combined effects of all inputs (direct and indirect) be understood in the context of their impact on biologic processes.

During these processes, a myriad of excretory, secretory, respiratory, hormonal, and other metabolic substances are produced. Many of these substances are potential biomarkers. Biomarkers have become central to clinical medicine, pathology, and molecular epidemiology.⁴⁰ Some biomarkers have utility in the diagnosis, treatment, and follow-up of disease. The utility of many others is under active investigation.^29,41–46

Finally, the SBIM posits that disease will only occur if the magnitude of impact produced by inputs and metabolic processes is sufficient to overwhelm bodily reparative, restorative, or compensatory mechanisms, causing the accumulation of genotypic, phenotypic, or psychologic abnormalities, which ultimately result in a disease state or health deficit. The challenge then for science is to use this model first to help organize and define the inputs, biologic processes, and outcomes that exist on each of the three suggested levels of exposure. The second challenge is to define how each of these factors relate to each other, again within the framework of a causal schema, to produce the outcome of interest. In other words, elucidate the relationships between inputs, processes and outcomes to produce the individual-level outcome (disease) or population-level outcome (disparity) of interest.

LUNG CANCER AND THE SBIM

The SBIM framework suggests that the development of lung cancer is preceded by one or more carcinogenic direct inputs (exposures). Environmental, behavioral, and occupational exposures to well known pulmonary carcinogens, including tobacco, asbestos, radon, polycyclic aromatic hydrocarbons (PAHs), and heterocyclic amines are well documented (Figure 2).^40,46,47

FIGURE 2.

This graphic employs the SBIM to outline a mechanistically driven framework for understanding socioenvironmentally associated lung cancer development on the cellular, individual, and population levels.

Next, indirect inputs modify these exposures. Potential indirect inputs of pulmonary carcinogens are many and as indicated by the model, operate on the cellular, individual, and population levels. Population-level indirect inputs could include certain geographic factors such as physical proximity of housing to a source of ambient air particulate toxicants. Individuals living in housing units located close to a factory spewing carcinogenic emissions from its smoke stack might be expected to experience higher carcinogenic exposure levels over time compared to ambient air exposures in individuals who live farther away from these sites. In fact, location of urban residence has been associated with increased personal exposure and an increased lifetime risk of cancer.^48,49 In addition, carcinogenic exposures from other sources like diesel exhaust fumes may be significantly higher in urban communities than exposures to these same carcinogens in rural environments. Scientific evidence does indeed document that the carcinogenic activity of some PAHs may be related to exposures not only from cigarettes, but also from other environmental sources.⁵⁰ Coke oven plant workers, commercial printers, truckers (diesel exhaust), and workers from rubber, asphalt, coal, and aluminum plants are all at increased risk of exposure to PAHs.^48,51,52 Occupational scientists have shown that increased work-related PAH exposure is associated with an increased risk of morbidity,⁵³ DNA damage,⁵⁴ abnormal methylation of tumor associated genes,⁵⁵ and increased risk of lung cancer.^56,57 Thus, both proximity of urban residence to a carcinogenic source and ambient air toxicant concentrations likely influence cumulative individual and population PAH exposure.

As one continues to think about potential lung cancer indirect inputs, the role of diet as an important factor must be considered. Whereas diet has often been considered an important carcinogenic exposure, the SBIM encourages a more explicit understanding of diet, dietary constituents, and their level of exposure. As such, it may be that specific dietary nutrients or dietary carcinogens would be considered inputs, whereas other sociocultural and regulatory factors that influence dietary choices of individuals would be considered indirect inputs. As such, “diet” may be considered a culturally influenced (and as such population-level) indirect input whereas the actual dietary constituents consumed may be seen as an individual-level direct or indirect input or depending on the specific constituent in question. For example, cruciferous vegetables of the Brassica family (broccoli, cauliflower, etc.) have been linked to their ability to affect the activity of CYP and GST enzymatic systems, thereby inhibiting phase I bioactivation of carcinogens and inducing phase II carcinogenic detoxification.^58–60 Thus, broccoli in the diet would be an individual-level indirect input, whereas the availability of broccoli at local grocery stores, the price of broccoli in those stores, or whether or not individuals would choose to eat broccoli would, on the other hand, represent societal (neighborhood), SES (market forces dictating prices), and cultural (certain cultures tend not to eat certain things) factors, which in the SBIM framework, would all represent population-level indirect inputs.

Local or national regulatory policy may also influence lung carcinogenesis at the population level by either indirectly influencing dietary food choices or by influencing carcinogenic exposures among populations. For example, consider that grocery store location, cost of foodstuffs, and quality of supermarkets have all been linked to dietary choices and nutrient availability.⁶¹ In the case of liquor establishments, store location and number of stores in a given community were shown to be associated with amount of alcohol ingested per capita in the local community and associated with dietary nutrient intake by local community residents.⁶² Regulatory policies then, such as business zoning and liquor licensing laws, may have the unintentional and unrecognized consequences of influencing cancer risk by impacting personal ambient air carcinogenic exposures via regulating the proximity and density of living establishments to urban and occupational particulate carcinogen sources and by influencing dietary nutrient availability of residents living in a given community.

Finally, in terms of indirect inputs, the carcinogenic potential of the PAHs may be impacted by the presence or absence of a given genetic polymorphism operating on the cellular level.^18,63–71 Researchers then could seek to elucidate the relationships between these multilevel phenomenon, which all operate along the lung cancer causal chain in individuals, to produce lung cancer or not and also among populations to produce a given lung cancer disparity.

It is true that completely characterizing the independent and joint effects of all potential direct and indirect inputs in complex mixtures, such as ambient air, tobacco smoke, and diet, or completely quantifying the effects of all important genes and polymorphisms impacting all requisite biologic processes is a formidable task.^72–74 However, it is clear that as many social and environmental sources as possible must be collectively considered, evaluated, and quantified to most accurately ascertain cumulative individual- and community-level carcinogenic exposure or risk.

Continuing with our model, the physiologic disposition of carcinogenic inputs occurs next. Here, the biomolecular experimental literature is replete with relevant mechanisms and several excellent reviews summarize the expansive current knowledge of biochemical and molecular genetic events involved in the metabolism of tobacco carcinogens.^75–79 As such, these will not be detailed. In brief though, over time, prolonged tobacco-related carcinogenic exposure is associated with progressive accumulation of phenotypic and genotypic abnormalities, leading to tumor initiation, promotion, and progression.^75–79 Consequent to these processes, many metabolites and byproducts are produced, released, or otherwise given off. Recent advances in molecular biology and genetics have made it possible to identify many of these potential lung cancer biomarkers.^45,80 The relative utility of these biomarkers in cancer prevention and prognostication is an active area of research.

Finally, according to the SBIM, lung cancer will occur if the combined effects of all important carcinogenic direct and indirect inputs are sufficient to cause the accumulation of phenotypic and genotypic abnormalities, such that tumor initiation, promotion, and progression will occur in individuals (disease) or populations (disparity). Thus, as can be seen from the preceding example, a single organizing framework such as the SBIM is needed to help organize the myriad of factors involved in lung cancer susceptibility and occurrence among individuals or to comprehensively explain differential outcomes between populations.

Health Disparities, Lung Cancer, and the SBIM

Given that the human genome has proven to be highly conserved, genotypic variation alone cannot adequately explain the existence of health disparities and socioenvironmental factors are now believed to be important in their development. In general, the SBIM model suggests that health disparities (a population-level outcome) occur when direct input–indirect input profiles (the sum total of the effects of all inputs) are sufficient to produce disease (lung cancer) in a higher than expected number of individuals in a given population. With regard to lung cancer specifically, the interplay of environmental factors, geography, smoking, and biology suggested by the SBIM may underlie findings such as those of Pastorino et al.⁸¹ who studied the relationship between occupational carcinogen exposure and cigarette smoking in lung cancer. His work found that in a general industrial worker population, occupational exposure to pulmonary carcinogens causes lung cancer in a cooperative and multiplicative fashion with increasing levels of cigarette smoking. Archer et al.⁸² reported similar findings among uranium miners who smoked. These studies demonstrated that occupational exposures conferred an increased risk of lung cancer in smokers and nonsmokers. This elevated risk, however, was further increased exponentially with increasing levels of cigarette smoking.

Racial and ethnic disparities in lung cancer offer another illustrative case in point. According to the recent Surveillance Epidemiology and End Results (SEER) data, African-American men have significantly elevated lung cancer incidence and mortality rates compared to white men (Table 1). African-American women, on the other hand, have only a slightly elevated lung cancer incidence and essentially the same lung cancer mortality rate compared to white women.⁸³

TABLE 1.

Recent SEER data depicting current racial and ethnic lung cancer disparities between African-Americans and whites in lung cancer incidence and mortality

US lung cancer incidence and mortality rates, 1992–1999*
	White	African-American
Lung cancer incidence
Men	82.9	124.1
Women	51.1	53.2
Total	64.3	82.6
Lung cancer mortality
Men	81.7	113.0
Women	41.1	39.6
Total	57.9	68.9

At first, no readily apparent, biologically plausible explanation for these findings is evident. The SBIM may suggest some scientific lines of inquiry or indicate some potential pathways. Indeed it is true that many causal pathways likely contribute to the observed population epidemiology. In addition, as stated above, accurate and precise risk characterization can only be accomplished after accounting for all environmental sources of pulmonary carcinogens and major mediators. Finally, causal pathways must work through biological mechanisms.

The SBIM suggests that at a minimum, precise lung cancer risk characterization must include an assessment of tobacco-smoking patterns, geographic factors, occupational exposures, and major potential indirect inputs. With regard to smoking habits, 2001 National Health Interview Survey data reveal that among working age individuals, overall smoking rates do not significantly differ between non-Hispanic African-Americans and non-Hispanic whites. The data also indicate that only relatively small gender differences in smoking prevalence exist (men=24.7, 95% confidence interval [CI] 23.9–25.6; women=20.8, 95% CI 20.1–21.5; whites=24.5%, 95% CI 23.8–25.2; and African-Americans=22.2%, 95% CI 22.1–23.3).⁸⁴ Epidemiologic data evaluating smoking trends in the US over the last three decades revealed that the prevalence of current smoking has consistently been highest among blacks and in black men in particular, with generally lower rates for women.^85,86 Men generally smoked more cigarettes per day than women, but overall, whites smoke more cigarettes than blacks.^85,86 Recent increases in smoking by African-American women, however, have led to cigarette consumption rates on par with African-American men.⁸⁶ Despite the higher number of cigarettes smoked by Caucasians, most African-Americans smoke the brands with higher tar yields per cigarette.⁸⁶ Finally, in the 1960s and 1970s the age at smoking initiation for women was approximately 4 years later than men. By the 1990s, this difference had been reduced to 2 years.⁸⁶ Thus, although cigarette smoking is associated with the majority of lung cancer cases today, epidemiologic evaluation of historic smoking patterns in the US do not easily help to explain racial differences in lung cancer incidence and mortality. Other potential factors, including occupation and place of residence, may be important in the genesis of observed lung cancer disparities.

Racial and ethnic minority workers are generally overrepresented in blue collar and service jobs while underrepresented in professional careers.⁸⁷ In many of these jobs, minority workers are differentially exposed to occupational carcinogens, resulting in disproportionate disease.⁸⁷ Also, significant proportions of racial and ethnic minority workers live in central cities, in close geographic proximity to industrial plants, or factories. Finally, men comprise the majority of workers who work in exposure-prone industries (miners, steel workers, and chemical industry workers). Individually, these findings do not suggest a unified causal pathway. However, by employing the SBIM to collectively understand how these contributing factors may collectively impact tumor biology, biologically plausible clues begin to emerge.

One possible pathway that is suggested by the SBIM is that differential exposures related to urban residence and occupation across racial and ethnic groups may act cooperatively to influence the genesis of lung cancer disparities. For example, at baseline, men smoke more than women and blacks smoke higher tar-yielding cigarettes compared to whites. In addition, this smoking-related risk elevation in African-Americans might be further heightened via ambient air carcinogenic exposures among African-Americans who live in the urban inner city. This could further elevate the risk of lung cancer in African-Americans above that of white Americans who are less likely to live in neighborhoods with elevated baseline ambient air carcinogen levels or smoke high tar cigarettes. Among men, African-American incidence and mortality rates may still be further increased through occupational carcinogenic exposures, which biologically act synergistically with smoking patterns and geographic exposures. Women, both African-American and Caucasian American, who comprise a substantially smaller proportion of the workers in exposure-prone industries would not have this additional exposure and thus may not be expected to have lung cancer incidence and mortality rates at par with African-American men. Finally, indirect inputs including insurance status, healthcare access, dietary factors, genetic polymorphisms, or regulatory policy may attenuate or potentiate this pathway as outlined in previous sections.

Discussion

It is interesting to note that scientific evidence suggests that disease causation in general and health disparities in particular result from complex interactions of many factors that simultaneously and often cooperatively act across more than one level of influence over time. An integrated understanding of disease or disparities causation would likely facilitate research and breakthroughs in treatments and interventions. Achieving such a goal in the current state of scientific inquiry is itself a difficult task, with several factors mitigating against such an accomplishment.

We first outline a multilevel, transdisciplinary organizing model and define the terminology used in reference to this model. Then using lung cancer as a case in point, we attempt to illustrate that this model provides a population-oriented, biologically grounded framework for understanding cancer etiology and pathogenesis. We also use the model to provide a mechanistic framework for understanding lung cancer disparities.

This model facilitates cross-disciplinary investigation and communication by providing a common conceptual model and terminology while articulating a biologically driven construct employing both sociobehavioral and biologic variables that influence disease pathogenesis. We acknowledge that some investigators will favor further subdivisions at each of the proposed levels of organization presented in this model. For example, social scientists may prefer that the population level be subdivided into family, neighborhood, and community levels. On the other hand, clinical scientists may want the individual level to be further subdivided into an “organ” level, whereas molecular scientists may seek a submolecular level to be added to the model. Whereas each of these modifications may make sense to a given investigator, they also may have no meaning to an investigator who works largely on a different level. For example, social scientists may not see a reason or value of further subdividing the individual or cellular levels of the model whereas molecular scientists may see no need for multiple subdivisions at the population level. Indeed, further subdivisions of the basic model may increase confusion across disciplines. As such, this model presents a basic three-level framework. Yet, the authors vigorously encourage individual scientists and groups to further subdivide the model as deemed appropriate to facilitate their investigations. In this way, it is hoped that this model may help science to move beyond only attempting to identify isolated “causes” of disease or isolated causes of health disparities (be they behavioral, biologic, or environmental), to also seeking to uncover patterns of behavior-biology interaction that positively or negatively affect individuals and populations. In so doing, we may then improve our understanding of health and disease at the interface of biology, behavior and the environment.