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Global Inequality. The world is extremely unequal. Life expectancy is Congo is 51 vs. France/Japan is 84. Tremendous range. Access to clean water – in Africa, very difficult. In US/Europe very easy. Champagne glass can help explain inequalities in wealth we see. It represents the distribution of wealth. Top 1/5th have 82.7% of the global income. Poorest 1/5th have 1.4% of global income. Richest 85 people in world have more wealth than the poorest 3.5 billion people in the world. Inequalities in individual countries as well, ex. very poor countries can have a few extremely rich people. Maternal mortality rate is a marker for healthcare systems. • In NA and Europe 10-20 people per 100 000 die of childbirth. • In SA 75/100 000 • SE Asia, 170/100 000. • Central Africa 700+/100 000. Heath and Healthcare Disparities in the US A lot of disparities we see in US are result of poor economic and environmental conditions. Social-economic status is a pyramid. • As we go up social pyramid, access and quality of healthcare improves. • Opposite is true for those at bottom of pyramid – more disease, less high quality healthcare, substandard housing, poor diet, dangerous jobs, can’t afford expensive treatments Race can play a role – Hispanics and African-Americans have higher morbidity and mortality rates, worse access to healthcare and lower quality healthcare. • Even though some can be attributed to SES reasons, doesn’t explain everything. Minorities less likely to receive everyday healthcare and treatments for life-threatening conditions. Gender differences – men typically use fewer preventative services like vaccines/check-ups. • Women require reproductive services, and access is reduced due to local laws. • Studies for treatments for diabetes/heart disease don’t always include women, and can suffer from lack of research. LGBT community – might face discrimination, which can limit clinics they feel comfortable seeking help from. • Transgender especially face discrimination, and have a hard time finding someone who has experience working with transgender individuals. Leads them to be reluctant to seek services when they really need them. Intersectionality – discrimination based on multiple factors Many types of discrimination, like sex/gender/culture/race, but what if someone experiences multiple forms at same time? Ex. Female who is African American and practices Buddhist teachings, causing her to be discriminated against in 3 different areas. Social Stratification – groups of people are given better preferences than others (group based) (intersectionality is at the individual/person level) Why is it important to consider intersection? Because multiple different categories of potential discrimination/oppression that compounds in one individual, and put her at disadvantage in society. Theory of intersectionality asks us to consider all the different levels of discrimination. Originally coined in 1989 by Crenshaw as a feminist theory, but has since expanded out and use it to explain oppression in all parts of society. Class Consciousness and False Consciousness. Means of production – way we produce goods, ex. Factories and farms. Owned by fairly wealthy individuals, which hire a large amount of workers which offer their labour, without owning any of the means of production. There’s a class divide, a hierarchy of upper/lower class. Theory by Karl Marx – workers in working class don’t realize they’re being exploited and oppressed by this capitalistic model of working. Workers can develop class consciousness, and realize they have solidarity with one another and struggle to overcome this oppression and exploitation. • Involves seizing and obtaining means and redistributing the means of production among the workers. False consciousness – unlike class consciousness, instead of seeing they have solidarity with one another, they’re unable to see their oppression. • And owners can promote this false consciousness by controlling classes, making it more difficult for workers to see their oppression. Statistics. Regression – all variables examined are continuous Linear regression – degree of dependence between one variable and another. Data is on scatter plot, one-way influence of one variable on another. Correlation - all variables examined are continuous. Unlike regression makes no assumptions about which variable is influencing the other. If correlation coefficient is 1, perfect. If -1, opposite. 0, random. Chi-square – when all variables are categorical, looks at if 2 distributions of categorical data differ from each other. Null hypothesis vs. alternative hypothesis. T-test – compares mean values of a continuous variable (dependent) between 2 categories/groups, ex. comparing mean of a group to a specific value. Can also compare means of 2 groups. Two-tailed = possibility of relationship in both directions, one-tailed = one direction. ANOVA – similar to t-test, compare distributions of continuous variable between groups of categorical variable, but can be used for 3+ groups. If value doubles, 100% increase Study Types. Cross-sectional study – look at a group of different people at one moment in time Cohort study – following a subset of population over a lifetime. A cohort is a group of people who share a common characteristic (ex. people born and exposed to same pollutant/drug/etc.) in period of time. Longitudinal study – data is gathered for the same subjects repeatedly over a period of time, can take years or decades. Case-control study – observational study where 2 groups differing in outcome are identified and compared to find a causal factor. Ex. comparing people with the disease with those who don’t but are otherwise similar. Clinical trial - highly controlled interventional studies Randomized Controlled Trial – people studied randomly given one of treatments under study, used to test efficacy/side effects of medical interventions like drugs. Gold standard for a clinical trial. Validity. Internal Validity – extent to which a causal conclusion based on a study is warranted. Confounding factors often impact the internal validity of an experiment. External validity – Whether results of the study can be generalized to other situations and other people. To protect external validity, sample must be completely random, and all situational variables must be tightly controlled. Construct validity – whether a tool is measuring what it is intended to measure. Regression to the mean – if first measurement is extreme, second measurement will be closer to the mean Confounding variables – changes in dependent variable may be due to existence of or variations in a third variable Temporal confounds – time related confounding variables Types of Control. Vehicular control – what experimental group does without the directly desired impact Positive control – treatment with known response Negative control – group with no response expected