NYU Health and Society in a Global Context Midterm

god help us all (it's actually a really easy class)

Epidemiology

study of distribution, causes, and effects of health and disease in defined populations

Primary Prevention

Prevent illness/injury from occurring

• infrastructure and laws (e.g., divided highways, speed limits, traffic lights)

Secondary Prevention

Minimize severity or damage after event has occurred

• Safer cars (e.g., air bags, seatbelts (and laws), bumpers, crush space)

Tertiary Prevention

Further minimize overall disability

• EMS, 911 system, trauma centers (location)

Prevalence

Number of people who currently have a disease

• often expressed as a proportion or rule

• implies a population at risk (denominator

Ex. —> 5% or 50/1000 persons

Incidence

Number of new cases/diagnoses

• Usually incorporate a time dimension because we need to know the rate at which new cases are arising

• Also incorporates dimensions used/implied in prevalence

Incidence of Death

= Death rate/Mortality rate

• e.g., 400 deaths/100,000/year

When diseases are highly fatal 

death rate > incidence rate (e.g., pancreatic cancer)

When many survive

death rate < incidence rate (e.g., breast cancer)

Chronic and incurable diseases

prevalence > incidence (e.g., HIV/AIDS, arthritis, diabetes)

Common and short-lived

incidence > prevalence (e.g., STDS)

Rapidly Fatal

incidence> prevalence (e.g., pancreatic cancer, acute leukemia)

Study Designs

• Cross-Sectional Study

• Prospective Cohort Study

• Longitudinal Data/Cohort Study

• Case-Control Study

Framingham Heart Study

Found that high blood pressure, high cholesterol, obesity, smoking, and physical inactivity are all risk factors for cardiovascular disease (CVD)

• Example of a Prospective Cohort Study

• There was no concept of “high blood pressure” (hypertension)

Cross-Sectional Study

Survey of a population where the exposure and disease status are determined at the same time

Defined Population —> Collect data on Exposure and Disease —> exposed w/ disease; exposed w/o disease; unexposed w/ disease; unexposed w/o disease

Prospective Cohort Study

• Start with a defined population (cohort)

• Some are exposed and some unexposed

• Follow up and compare development of disease

Defined Population —> Expose; Unexposed —> Develop Disease/Do Not Develop Disease; Develop Disease/Do Not Develop Disease

Longitudinal/Cohort Study

• Advantages are cross-sectional data

  • can study incidence and prevalence

  • can show a temporal relationship

  • can access change within-person

• Limitations

  • Can require very long wait time

    • Behaviors —> Heart disease

  • Can be very costly

  • Impractical when disease is rare (case-control is better)

  • Attrition (drop-out)

Case-Control Study

• Examine relation of an exposre to disease

• Identify people w/ disease (cases) who match to people w/o disease (controls)

• Compare w/ respect to prior exposure status

Disease (Case) —> Exposed/Not Exposed; No Disease (Control) —> Exposed/Not Exposed

Primary Distinction Between Case-Control Study and Cohort Study

Case-Control Study starts with cases rather than a cohort (pre-defined population)

Case-Control Study saves time over Cohort Study. Why?

They are retrospective. They start with individuals who already have the disease (cases) and look backward at past exposures, eliminating the long, forward follow-up time required by cohort studies to wait for the disease to develop.

Confounding

Associations may not reflect causation because of confounding variables

Confounding Variables

Hypothesize X causes Y —> Conduct an epi. study and observe that X and Y are correlated

• it might be the case that X causes Y

• or is it due to the relationships of X and Y to other variables (Z)

Confounding Variables

Vitamin F X —> Y lower cancer risk

Z (could be healthy lifestyle, exercise, SES, sex) —> X----Y

How to address Confounding Variables

• Statistical Adjustments

• Restrict Sample

• Randomization - Experimental Studies

Criteria for Causal Associations

• Temporal Relationship

• Biological Plausibility

• Replication of Findings

• Extent that alternate explanations have been considered

• Consistency w/ other knowledge

p-value

Probability that the observed result could have occurred by chance alone

• Statistically significant usually = p < 0.05

P =

Chance that the real difference = 0

Risk of Disease

Risk Among Exposed/Risk Among Not Exposed

Relative Risk of Disease

A/(A+C) / B/(B+D)

Confidence Interval

Range of values within which the true result probably falls

• Typical threshold is 95% CL

• “We are 95% confident that the true (mean, or something) lies between (#, #)

False Positive

Finding an effect when there really isn’t one

False Negative

Failing to find an effect when there really is one

Random Error

Using p<0.05, 95% CL

Systematic Error

Confounding Variable(s)

Betting Your Life (Trillin)

Article from The New Yorker that talks about a woman who is able to get access to care because of her privilege

Quantifying the Burden of Disease

Different Measures offer varying perspectives:

• Deaths

• Years of Life Lost (YLL)

• Years Lived with Disability (YLD)

• Disability-Adjusted Life Years (DALYs)

DALYs

• Attempts to combine mortality and morbidity into a single measure

• Years of “healthy life” lost from:

  • Premature Death (YLL)

  • Being in a State of Poor Health or Disability (YLD)

• DALY = YLL + YLD

YLL = 

N x L

• N = # of Deaths

• L = Standard Life Expectancy at Age of Death (years)

YLD =

I x DW x L

• I = # of incident cases

• DW = Disability (0-1)

• L = Average Duration of Case until remission or (years)

Social Constructionism

A conceptual framework emphasizing the cultural, political, social, and historical aspects of how we understand phenomena

• Meaning is not naturally born, is the product of interaction in a social context

Cultural Meaning of Illness

• Stigmatized conditions

  • e.g., mental illness, epilepsy, cancer, HIV/AIDS, STDS

• Contested Conditions

  • e.g., chronic fatigue, fibromyalgia, IBS, multiple chemical sensitivity

  • “illness you have to fight to get”

• Disability

  • Distinction between the impairment (the attribute) and the “disability” (the social experience and meaning of the impairment)

Policy or Practice Implications for Cultural Meaning of Illness

• Patients have to seek treatment

• Physicians don’t take it seriously

• Insurance coverage

• Disability Benefits (contested states)

• Research Funding

• Societal Accommodations for Disability (ADA)

• Opposition to cochlear implants, genetic screening/selection, etc.

Illness as Socially Constructed

• People enact their illness and endow it w/ meaning

• Not merely passive entities to whom things are done (be it by a disease or by doctors and treatments)

• e.g., management of seizure disorders, illness identities (cancer survivor), personal narratives, etc.

Policy or Practice Implications for Illnesses as Socially Constructed

• e.g, “Noncompliance” + focus on meaning of mediations (or interventions in the context of everyday life rather than simply compliance w/ doctor’s orders

Medical Knowledge as Socially Constructed

• Influence of gender, race, class

• e.g., behaviors during pregnancy, PMS

• Medicalization

  • Deviance: drug and alcohol problems, homosexuality

  • Menopause: childbirth, menstruation

  • Role of Pharm. industry (Moynihan)

Medicalization

• Human problems or experiences become defined as medical problems, usually in terms of illnesses, diseases, or syndromes… and medical intervention becomes the focus of remedy

  • Deviance: drug and alcohol problems, homosexuality

  • Menopause: childbirth, menstruation

  • Role of pharm. industry (Moynihan)

Policy or Practice Implications for Medical Knowledge as Socially Constructed

• Encourages medical solutions while ignoring or downplaying the social context of complex problems

  • e.g., gastric bypass for obesity

  • treating PMS w/ antidepressants

  • Examples from Moynihan

• Attribution of Responsibility (Chang) 

• Health care reform: ever larger jurisdiction of medical problems that are subject to coverage

Selling Sickness (Moynihan)

• Ordinary Processes or Ailments as medical problems (baldness)

• Mild symptoms as portents of serious disease (IBS)

• Personal or social problems as medical ones (social phobia)

• Risks conceptualized as diseases (osteoporosis)

• Disease prevalence estimates framed to maximize the size of a medical problem (erectile dysfunction) 

Attribution of Responsibility

How accountability for health outcomes is assigned, which can be directed toward the individual, social networks, or the broader society

Social Conditions as a Fundamental Cause of Disease

Link and Phelan come up with the Fundamental Cause Theory which posits that SES and health outcomes are positively correlated

• Higher SES → More Resources → Better health outcomes

• Resources = power, money, knowledge, social connections

Fundamental Cause Theory

• Higher SES → More Resources → Better health outcomes

• Lower SES —> Less Resources —> Worse health outcomes

• Resources = power, money, knowledge, social connections

(Link and Phelan)

Disease Mongering

The practice of widening the boundaries of treatable illness to expand markets for pharmaceutical treatments, replacing social construction of illness with corporate construction of disease.

(Moynihan)

Women are…

More likely to seek care than men and generally live healthier lifestyles

• Women are more likely to have health insurance than men (also has to do with income and women are more likely to qualify for Medicaid)

• Men are more likely to consume alcohol and cause more alcohol-related incidents than women

Obesity =

Excess caloric intake over time