Anita Kothari
akothari@mail.health.uottawa.ca
Not for distribution without permission from the author
December 2002
A COMMENTARY ON CONTEXTUAL INFLUENCES ON HEALTH
For some time now researchers have suggested that traditional models of health and
health research ought to be supplemented with more context-sensitive approaches (Diez-Roux
1998; Link & Phelan 1995; McKinaly 1993; Stokols 1992; Susser, Watson, et al. 1985; Syme
1986; Syme & Berkman 1976). These models are applied to the ways in which:
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health is conceptualised
poor health is thought to be caused
health (and illness) is studied
the effectiveness of interventions is assessed
and to the ways in which findings from health research are implemented in
programs and policies
Contextual approaches are concerned with understanding the surrounding circumstances
alongside with understanding the associated health problem or issue. This approach is in
contrast to traditional reductionist models of health, which require that health-related problems
be broken down into smaller units of analysis. Such subproblems then become available for
study in their own right, and subsequent research findings associated with a subproblem are
generalized to the original problem.
For example, instead of explaining ill health as inadequate health care services
(reductionist approach), ill health can be described using frameworks that integrate, and consider
the interrelatedness of, the social, physical and economic environment and declining health status
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(contextual approach) (Evans & Stoddart 1990). In terms of conducting research, contemporary
epidemiologists seek out individual risk factors for diseases (reductionist approach). In contrast,
non-reductionists recommend a return to a more classical type of epidemiology, where people,
problems and settings are studied as a whole (contextual approach) (Pearce 1996). Contextsensitive approaches can also be identified in the application of research findings. For example,
the practice of public health has experienced a shift in tradition; the Anew public health@ focuses
on broader-based social interventions as a complement to trying to modify individual risk
behaviours (Dean & Hunter 1996).
In the desire to overcome reductionist tendencies among health researchers, the
advantages of a contextual approach have been presented from various angles (Diez-Roux 1998;
Link & Phelan 1995; McKinaly 1993; Rose 1985; Stokols 1992; Susser, Watson, et al. 1985;
Syme 1986; Syme & Berkman 1976). One of the arguments is that current interventions mostly
concentrate on Adownstream@ acute care needs, but greater emphasis needs to be placed on
Aupstream@ ones that target overarching, persistent conditions (e.g., social and political
structures) (Link & Phelan 1995). Another argument is that there has been little success with
changing individual lifestyle behaviours. Therefore, changing contexts to facilitate healthy
behaviours in populations might be more successful (Rose 1985; Syme 1986). A related
argument is that individually based strategies encourage Avictim-blaming@ and dissociate the
social component of health-related behaviours (Emmons 2000). In light of these and other
arguments, health researchers have been encouraged to devote attention to contextual elements
when designing research studies.
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Trying to incorporate contextual sensitivity in health research is a tricky matter, I have
discovered. My attempts in this direction (Kothari 2002) have uncovered three issues with
respect to the approach.
Three Concerns About the Contextual Approach
The first concern is that while the literature contains many arguments for doing contextsensitive research, there are few empirical examples that treat contextual conditions in a truly
meaningful way. Those that treat it at all tend to include context as simply one more isolated
variable (e.g., Anderson, Sorlie, et al. 1996). The danger is in slipping into traditional modes of
conceptualising that fail to relate contextual variables to other variables. This could be avoided
by the use of a comprehensive conceptual framework, through the analytical investigation of
interaction effects, and/or by engaging in a thorough discussion of study findings. That is, what
is required is an approach that reflects the nature of society, where sub-populations are in fact
nested within, and influenced by, other populations and environments. Without acknowledging
the interrelatedness of variables, researchers and policymakers might perceive individual and
contextual determinants of a phenomenon as two completely separate, independent effects.
What is more meaningful is hypothesizing an overlap or interaction between the two effects, and
then seeking to understand the nature of this interaction.
The next concern relates to a definition of context. Discussions in the health research
literature revolve around the measurement and analysis of context, and less around what is meant
by context. This lack of discussion has resulted in a default position: that context is the social,
physical or economic environment. The default position has its place, but it is limited, and
should be supplemented by other possibilities. Informal statistical parlance likens context to the
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background Anoise@, and outcomes as the Asignal@ in a radio transmission. In some situations,
the noise or context could be the social, physical or economic environment. In other situations
the noise could mean key legislation, local politics or historical context, as they affect health
outcomes. For example, determining the success of tobacco control initiatives could be well
informed by studying legislation, politics or historical background. I propose, then, that there is
room for discourse around what context might mean for health research methods and subsequent
population-based intervention programs.
The last concern is that despite the recommendations to consider context, the health
literature is just starting to make progress in terms of promoting innovative ways to conduct and
apply context-sensitive research. This progress is exemplified by the growing literature on
multilevel modelling in health research. This analytical technique permits the modelling of
separate and joint effects of contextual and individual level factors on an outcome. Interested
readers are direct to studies on smoking (Duncan, Jones, et al. 1993; Duncan, Jones, et al. 1999),
drinking (Duncan, Jones, et al. 1993), low birth weight (O'Campo, Xue, et al. 1997),
cardiovascular disease (Diez-Roux, Nieto, et al. 1997; Diez-Roux, Link, et al. 2000), and health
status (Robert 1998), to name a few.
Where Do We Go From Here?
A difficulty in incorporating a contextual approach is the minimal number of examples or
specific guidance in the health research literature. This gap in the literature is especially relevant
to health promotion and disease prevention activities, where the phrase Aweb of causation@ is
often used to describe the complexities of public health programs. Such interventions are likely
to be delivered in ways that are influenced by local conditions. And these interventions are
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subsequently taken up or practised by an individual who is also heavily influenced by his or her
circumstances. If context influences the delivery, practice, outcome or value of an intervention,
then the extent of that influence needs to be taken into account before the intervention reaches its
target audience. Otherwise, researchers, decision-makers and program recipients might assume
an inaccurate measure of effectiveness (Birch 1997).
The contextual approach is accompanied by many methodological challenges: an
appropriate context must be decided upon, information from the context needs to be collected,
corresponding information from the individual level must also be collected, and the data
analyzed such that proper inferences can be made at both the contextual and individual levels.
We must start somewhere, however, if only to chip away at the problem. To begin, two healthrelated areas – multilevel modeling and population health/health promotion – provide some key
ideas that have the potential to contribute to a broader discussion about contextual issues and
research.
Contextual Learnings from Multilevel Modeling
Multilevel modeling is an extension of regression modeling, in which two levels of data
(or more) are modeled simultaneously but separately. In this way the influences at both levels –
individual and contextual – on health outcomes can be compared. The details of the statistical
technique are less important for the purposes of this discussion. What is important is how the
application of this technique has shaped our thinking about the hows and whats of contextual
effects.
Contextual differences do not necessarily mean a contextual effect. The specific steps
associated with a multilevel analysis highlight the point that differences in outcome by
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contextual unit (e.g., differences in smoking behaviour by health region) are only that: variations
by area, not necessarily effects due to area.
Some contextual effects are due to people. Drawing from the example above, differences
in smoking behaviour by region may have little to do with the region per se, and more to do with
the regional concentration of people with a particular social profile.
Some contextual characteristics might demonstrate an effect on the outcome. There
might be characteristics of the region that influence smoking behaviour, regardless of the types
of people living within. An example here is the effect of rural (in contrast to urban) areas.
Contextual characteristics may exhibit different “doses” of effect. In other words,
contextual effects need not be uniform among contextual units – different rural areas may exhibit
different degrees of effect on smoking behaviour.
Contextual characteristics may affect (poor) people differently. There might be
differences in contextual effects within a region, depending on the social profile of the smoker in
question. A poor person in a rural area might smoke more than his or her richer neighbour.
Are administrative boundaries from health surveys meaningful? Most multilevel studies
conveniently use data from census or related geographic areas. Whether a census division is the
most meaningful geographic unit for thinking about the causes of health outcomes, however, is
currently under discussion.
Contextual Learnings from Population Health/Health Promotion
The areas of population health and health promotion also provide some helpful insights to
incorporate into context-sensitive approaches. These insights are informed in part by grassroots
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community health program experiences. The population health/health promotion literature
reminds us that:
Conceptual models are important. Models require that pathways to health outcomes are
explicit, whether through contextual influences, individual level influences or joint effects. In
turn, conceptual models facilitate the specification of hypotheses, and force researchers to
consider meaningful contextual units.
People can influence their contextual setting. Individuals can shape communities
through establishing social norms, supporting particular political structures or establishing
resources. Little attention has been given to the ways in which individuals influence contexts.
Contextual characteristics can be harnessed. Multiple intervention programs demonstrate
that contextual factors, which are often thought of as nuisance factors (e.g., permissive
community attitudes towards smoking) can also be harnessed to enhance interventions (e.g., antismoking by-laws working hand in hand with smoking cessation programs).
Issues of equity cannot be ignored, and contextual influences may have different effects
on different people. In some cases these effects might be detrimental to sub-groups within a
region, and/or encourage inequalities in outcomes between different sub-groups (e.g., antismoking by-laws may only be helpful for those belonging to higher socioeconomic groups).
Some Final Thoughts
The aim of this discussion was to highlight issues associated with contextual approaches.
Some key ideas were generated from two different areas of health; in fact, there is little overlap
among the key ideas – they belong uniquely to each area. This is good news for those interested
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in context-sensitive research, as a synthesis of the two perspectives yields greater problemsolving potential.
Taking a step back, what is being suggested implicitly in this commentary is the need to
complement generalized knowledge with contextual knowledge. Hopefully the more explicit
points discussed above will promote some meaningful discourse about what context is, and how
the health research community can study it more effectively. And as research experience
accumulates, what will need to be demonstrated is whether this combination of knowledge
contributes to a healthier patient, and to a healthier population.
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References
Anderson RT, Sorlie P, Backlund E, Johnson N, Kaplan GA. Mortality effects of community
socioeconomic status. Epidemiology 1996; 8(1):42-47.
Birch S. As a matter of fact: Evidence-based decision-making unplugged. Health Economics
1997; 6(6):547-549.
Dean K, Hunter D. New directions for health: towards a knowledge base for public health action.
Social Science and Medicine 1996; 42(5):745-750.
Diez-Roux AV. Bringing context back into epidemiology: Variables and fallacies in multilevel
analysis. American Journal of Public Health 1998; 88(2):216-222.
Diez-Roux AV, Link BG, Northridge ME. A multilevel analysis of income inequality and
cardiovascular disease risk factors. Social Science and Medicine 2000; 50:673-687.
Diez-Roux AV, Nieto FJ, Muntaner C, Tyroler HA, Cornstock GW, Shahar E et al.
Neighborhood environments and coronary heart disease: A multilevel analysis. American
Journal of Epidemiology 1997; 146(1):48-63.
Duncan C, Jones K, Moon G. Do places matter? A multi-level analysis of regional variations in
health-related behaviour in Britain. Social Science and Medicine 1993; 37(6):725-733.
Duncan C, Jones K, Moon G. Smoking and deprivation: are there neighbourhood effects? Social
Science and Medicine 1999; 48:497-505.
Emmons KM. Health behaviors in a social context. In: Berkman LF, Kawachi I, editors. Social
Epidemiology. New York: Oxford University Press, 2000: 242-266.
Evans RG, Stoddart G. Producing health, consuming health care. Social Science and Medicine
1990; 31(12):1347-1363.
Kothari A. The contextual approach in health research: Two empirical studies [dissertation].
Hamilton (ON): McMaster University; 2001.
Link BG, Phelan J. Social conditions as fundamental causes of disease. Journal of Health and
Social Behavior 1995; (Extra Issue):80-94.
McKinaly J. The promotion of health through planned sociopolitical change: Challenges for
research and policy. Social Science and Medicine 1993; 36(2):109-117.
O'Campo P, Xue X, Wang M, O'Brien Caughy M. Neighborhood risk factors for low birthweight
in Baltimore: A multilevel analysis. American Journal of Public Health 1997; 87(7):1113-1118.
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Pearce N. Traditional epidemiology, modern epidemiology, and public health. American Journal
of Public Health 1996; 86(5):678-683.
Robert SA. Community-level socioeconomic status effects on adult health. Journal of Health and
Social Behavior 1998; 39(March):18-37.
Rose G. Sick individuals and sick populations. International Journal of Epidemiology 1985;
14(1):32-38.
Stokols D. Establishing and maintaining healthy environments. American Psychologist 1992;
47(1):6-22.
Susser M, Watson W, Hopper K. Social class and disorders of health. Sociology in Medicine.
New York: Oxford University Press, 1985.
Syme SL. Strategies for health promotion. Preventive Medicine 1986; 15:492-507.
Syme SL, Berkman LF. Social class, susceptibility and sickness. American Journal of
Epidemiology 1976; 104(1):1-8.
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