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Medical Anthropology
Medical anthropology is a subdiscipline within anthropology that addresses sociocultural dimensions of health and illness, as well as the epistemologies and practices associated with diverse systems of healing. This entry examines medical anthropology's contribution to the study of the social production of health and illness. Although medical anthropologists move through the terrain of human health in various ways, this entry concentrates on a select few examples of theoretical and methodological contributions to the anthropological understanding of the political economy of health.
According to medical anthropologist Morgan (1987), the political economy of health is “a macroanalytic, critical, and historical perspective for analyzing disease distribution and health services under a variety of economic systems, with particular emphasis on the effects of stratified social, political, and economic relations within the world economic system” (p. 132). Political-economic medical anthropologists argue that health-threatening conditions are the result of historically based social, political, and economic systems of inequality. This perspective is bolstered by a methodological and conceptual commitment to the delineation of structures of inequality and their reproduction over time. The political-economic medical anthropology (PEMA) research framework thus engages the notion of change as an important part of the context of health.
PEMA research focuses on ideological and material foundations of inequality by examining the lived experiences of class relations and state-sponsored policies and practices. It expands microlevel, culturally based analyses of health and illness in particular communities or societies by illuminating the broader context through which health phenomena are unevenly distributed among social groups. PEMA research thus explores the interaction among macrosocial forces and microlevel circumstances. As a result, PEMA studies emphasize the multifactorial nature of disease causality.
According to Morsy (1996), power is the central analytical construct within the PEMA framework. Power is a relational concept that describes the privileges of one group and concomitant subordination of others. Analyses of power allow researchers such as Ida Susser to make the connections between material and social resource distribution and structures of social inequality. In turn, focusing on the various and interconnecting dimensions of power enables PEMA scholars to understand population health and sickness as socially produced phenomena.
Political Economy in Population Health
On the basis of this political economic framework, PEMA scholarship raises a variety of questions about the social mechanisms of health and illness. Some biocultural anthropologists work to understand how environmental, social, and biological factors interact to produce differential health outcomes and the uneven spread of disease in populations. As Wiley (1992) explains, these researchers discern the effects of inequality on population health by focusing on biological variation and change that is associated with disease, psychophysiologic symptoms, and malnutrition. For example, Dressler (2005) elucidates the connections between hypertension, stress, and structurally mediated culture change for African Americans and Brazilian families. He finds that downward social mobility and the resulting inability to fulfill culturally based consumer norms induce stressrelated illnesses such as hypertension and depression within these populations.
Another emerging trend in biocultural research involves the conceptual integration of social and human biological processes to examine health and illness as the direct outcome of political economic inequities. In their edited volume, Goodman and Leatherman (1998) provide a framework for applying political economic perspectives to biocultural studies. The contributions to this volume engage with diverse topics such as malnutrition, infant mortality, epidemic disease, and the impact that illness has on household production and reproduction. Swedlund and Ball (1998), for example, argue that poverty was the root cause of high infant mortality rates in a northeastern United States town in the early 20th century. Early studies that did not take into account the regional political-economic context blamed child death on the mothering skills of poor womenratherthannutritional and ecological factors. Like Swedlund and Ball, other contributors working primarily on communities across the Americas illustrate how political-economic processes influence human biology, producing biological variation among groups that is expressed as illness and disease. Subsequently, human biological events affect the social fabric of communities and societies.
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- Behavioral and Social Science
- Acculturation
- Bioterrorism
- Community Health
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- Cultural Sensitivity
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- Ecological Fallacy
- Epidemiology in Developing Countries
- EuroQoL EQ-5D Questionnaire
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- Genocide
- Geographical and Social Influences on Health
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- Health Disparities
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- Health, Definitions of
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- Medical Anthropology
- Network Analysis
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- Poverty and Health
- Quality of Life, Quantification of
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- Pollution
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- Radiation
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- Social Capital and Health
- Social Hierarchy and Health
- Socioeconomic Classification
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- Stress
- Teratogen
- Thalidomide
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- Urban Sprawl
- Vehicle-Related Injuries
- Violence as a Public Health Issue
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- Gestational Age
- Intent-to-Treat Analysis
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- Latency and Incubation Periods
- Life Course Approach
- Malnutrition, Measurement of
- Medical Anthropology
- Organ Donation
- Pain
- Placebo Effect
- Preclinical Phase of Disease
- Preterm Birth
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- Quarantine and Isolation
- Screening
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- African American Health Issues
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- Epidemiology in Developing Countries
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- Immigrant and Refugee Health Issues
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- Men's Health Issues
- Oral Contraceptives
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- Women's Health Issues
- Statistics and Research Methods
- F Test
- p Value
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- Analysis of Covariance
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- Bayes's Theorem
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- Binomial Variable
- Birth Cohort Analysis
- Box-and-Whisker Plot
- Capture-Recapture Method
- Categorical Data, Analysis of
- Causal Diagrams
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- Censored Data
- Central Limit Theorem
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- Cluster Analysis
- Coefficient of Determination
- Cohort Effects
- Collinearity
- Community Trial
- Community-Based Participatory Research
- Confidence Interval
- Confounding
- Control Group
- Control Variable
- Convenience Sample
- Cox Model
- Critical Value
- Cumulative Incidence
- Data Management
- Data Transformations
- Decision Analysis
- Degrees of Freedom
- Dependent and Independent Variables
- Diffusion of Innovations
- Discriminant Analysis
- Dose-Response Relationship
- Doubling Time
- Dummy Coding
- Dummy Variable
- Ecological Fallacy
- Economic Evaluation
- Effect Modification and Interaction
- Factor Analysis
- Fisher's Exact Test
- Geographical and Spatial Analysis
- Graphical Presentation of Data
- Halo Effect
- Hawthorne Effect
- Hazard Rate
- Healthy Worker Effect
- Hill's Considerations for Causal Inference
- Histogram
- Hypothesis Testing
- Inferential and Descriptive Statistics
- Intent-to-Treat Analysis
- Internet Data Collection
- Interquartile Range
- Interrater Reliability
- Intervention Studies
- Interview Techniques
- Kaplan-Meier Method
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- Matching
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- Multivariate Analysis of Variance
- Natural Experiment
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- Nonparametric Statistics
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- Null and Alternative Hypotheses
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- Pearson Correlation Coefficient
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- Person-Time Units
- Pie Chart
- Placebo Effect
- Point Estimate
- Probability Sample
- Program Evaluation
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- Qualitative Methods in Epidemiology
- Quasi Experiments
- Questionnaire Design
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- Relational Database
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- Secondary Data
- Sensitivity and Specificity
- Sequential Analysis
- Simpson's Paradox
- Skewness
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- Stratified Methods
- Structural Equation Modeling
- Study Design
- Survival Analysis
- Target Population
- Time Series
- Type I and Type II Errors
- Unit of Analysis
- Validity
- Volunteer Effect
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