It is an epidemiological axiom that data can be reported according to categories labeled person, place, and time. A popular text from the 1980s, Lilienfeld and Lilienfeld's Foundations of Epidemiology, begins this way: "Epidemiology is concerned with the patterns of disease occurrence in human populations and the factors that influence these patterns. The epidemiologist is primarily interested in the occurrence of disease by time, place, and persons" (1980:3).
What characteristics do scientists identify as falling within these categories, and what do they leave out? Some of the common variables that epidemiologists think of as belonging to the category person include age, sex, marital status, race, socioeconomic status, religion, and occupation. But each of these variables potentially represents multiple underlying processes. A variable such as age, for example, represents a biological process of growth and development as well as a social process of changing recognition of status and responsibility. A variable such as religion represents a set of conditions (presence of faith itself, behaviors dictated by church doctrine, access to social support, ability to attend services) as well as a routinely collected marker of social status. Without explicit theories linking underlying processes to measured causal variables, the categories are meaningless, and studies linking them to health outcomes are difficult to understand and compare.
Similar complexities exist within other categories of disease patterns. When researchers describe disease risk associated with place they include social or political boundaries such as neighborhood, state, region, and nation, but they also - intentionally or not - include geological or other physical environmental influences such as altitude, level of sunlight, fluoride or arsenic content in water, or particulate or carbon monoxide content in air. The social environment also can manifest itself spatially, as in population density and urban/rural lifestyle differences or in those place-based effects of social stratification such as quality of police, fire, schooling, or medical services. Other variables related to place could include aspects of the biological environment (presence of mosquitoes, spores, or toxic plants). Once again, some of these variables represent specific biological exposures (air quality, radiation, micronutrient levels in local food), whereas others cover more complex interrelated influences (mobility, job availability, and the quality of schools and medical services).
The category of time also covers a wide set of processes. Calendar time measured as days, years, or other periods plays its own role in disease distribution, measured through variables such as the time between moment of exposure and appearance of symptoms, or duration of infectivity, or age at onset, or life expectancy after onset. But the cultural practice of dividing time into weekdays versus weekends itself influences disease, since the meaning of these time periods structures activities like drinking, sexual activity, recreation, and work. Time can be a marker of biological influence: for example, in the seasonality of disease fluctuations due to underlying variations in number of mosquitoes (for malaria or yellow fever), ticks (for plague or Lyme disease), or infected raccoons (for rabies). Time also enters studies through so-called cohort effects, where people born during a certain period or of a certain age have similarly patterned illnesses. And time as history also influences health research, both through changes in diagnosis or terminology and through changing patterns of behavior (popularity of smoking, age at first sexual experience) or even the changing significance of specific life-cycle achievements. For example, getting married or obtaining a high school degree elevated the status of young adults more in the 1950s than it did in the 1990s.
The categories of person, place, and time sometimes overlap. Migration is perhaps the best example because it involves persons changing places over time. Studies of migrants have had particular force in the attempt to discover the causes of obesity, hypertension, and coronary heart disease. For example, comparing populations of Japanese men in Japan with Japanese men in Hawaii and California allowed researchers to hold genetic variability constant while looking at the effects of changing diet and other aspects of acculturation (Marmot and Syme 1976). In similar fashion, Janes (1990) was able to see the health effects of migration among Samoan migrants to California.
A. Mixing Person, Place, and Time: Modernization, Cultural Consonance, and Blood Pressure
What processes actually change as social conditions change or as people move to places where they encounter new conditions? Some possibilities include accumulation of goods, wage labor instead of subsistence labor, more time in formal education, loss of so-called traditional values and communal knowledge, changing diets and levels of physical activity, and acquisition of new norms and values. Studies by John Cassel and others proposed that changes in social conditions (urbanization, economic development, migration) led to greater stress and higher blood pressure.
Anthropologists and epidemiologists have investigated the health effects of such processes in numerous sites around the world, paying particular attention to migrants because they can be compared with those who stay at home (a baseline population) and because migrants usually change their behaviors and values as part of their adaptation to new sites. These studies show that the average blood pressure in the population tends to increase with modernization. These differences persist even when age and obesity are taken into account; they also tend to be larger among males than among females.
In attempting to understand discrepancies in hypertension rates between groups in complex, industrialized societies, anthropologist William Dressler and colleagues use the concept of "intracultural diversity" to emphasize that not all people who share a culture attach the same meanings to events or conditions (Dressler et al. 1996). What looks like a golden opportunity to one person might look like unseemly self-promotion to another, and the attributes and trappings of success also vary from person to person. Dressler argues that the existence and perception of stratification can have measurable health consequences; much of his work uses hypertension as a proxy measure of overall health.
Dressler is following some of the connections between social stratification and health that have been explored by the social epidemiologist Richard Wilkinson (1996) and others (Davey-Smith et al. 1990, Kawachi et al. 1997, Marmot et al. 1991). For example, Wilkinson argues that relative poverty, the size of the gap between rich and poor, not absolute poverty, is what best predicts high mortality and reduced life expectancy in industrialized countries. Unlike Wilkinson, Dressler explores what he calls "lifestyle incongruity": the health-reducing effects of attempting to maintain an unaffordable lifestyle (Dressler 1999). The flip side of "lifestyle incongruity," called "cultural consonance in lifestyle," measures the extent to which individuals are able to live in accordance with locally defined material standards.
In a series of studies of rural-to-urban migrants in Brazil, Dressler and colleagues first established which components of a material lifestyle were locally defined as most important, then whether people were able to achieve those ideals (1996). They found that blood pressure declined systematically as "cultural consonance in lifestyle" increased. They have also showed that the negative effects of "lifestyle incongruity" can be mitigated by strong social networks. Blood pressure is consistently lower -across societies - among individuals who enjoy good social support, even though the types of avoidance and coping strategies people use to deal with social and psychological pressures vary from place to place (Dressler et al. 1997). They posit that the fit between lived experience and perceived community standards has direct and indirect beneficial effects on blood pressure.
Dressler's paradigm offers an alternative response to those eager to see anthropologists map cultural groupings for epidemiologic purposes. Even if the boundaries of cultural areas cannot be defined with any precision, it is still possible to include these types of "cultural consonance" measures to determine who in a group holds shared values. This information can then be used to define group membership along the lines of present status, belief, or practice rather than residence within a zone of presumed like-minded souls.
B. Connecting Person, Place, and Time: Disease Clusters
Epidemiologists must sometimes investigate whether the appearance of disease among a group of people in a delimited space at a particular time represents levels of disease greater than expected. One famous contemporary disease cluster is the child leukemia cases found in one neighborhood in Woburn, Massachusetts, popularized in the 1995 book and subsequent film called A Civil Action. Another is the illnesses and deaths in 1976 among a group of American Legion war veterans meeting at a hotel in Philadelphia, which gave rise to the name Legionnaire's Disease. Clustering of disease in one place is often thought by the public to indicate an infectious or environmental cause (air or water pollution, radioactivity, or energy from high-tension power lines), but epidemiologists and biostatisticians recognize that some disease clustering can be expected to occur just by chance, with no underlying common cause (Schinazi 2000). Clusters are not always easy to identify. For example, the movement of people into and out of areas creates complex and confusing exposure dynamics: a group of residents at any one time may include a few recent arrivals with only brief exposure to local environmental risks, and it will exclude those with long exposure who have already moved.
Disease clusters offer another opportunity to see the interplay among person, place, and time. Yet the very concept of a "cluster" depends partly on a series of political and social conventions. The way that boundaries are drawn around administrative space can determine the denominator. For example, six cases of childhood leukemia counted within a residential block looks more like a cluster than six cases in a census tract or town. These conventions also affect which diseases are thought to be rare, based, for example, on individuals or governments deliberately misleading people about disease status. Social interactions may influence whether knowledge of common diseases is shared in the first place (illness reported within members of a church or students in a school versus illness unknown because it occurs among isolated or marginalized individuals). Finally, political and social conventions influence the period of time over which a cluster is studied, as well as the duration and intensity of data collection. For example, when the SARS (Sudden Acute Respiratory Syndrome) epidemic was identified in early 2003, Toronto officials reported cases accurately but lobbied hard against World Health Organization (WHO) travel warnings imposed on their city, while Chinese authorities suffered devastating consequences for a much longer period of time as a result of lapses in surveillance and their unwillingness to report cases candidly to international health agencies.
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