Bms 1042 Notes

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– Ordinal (order of categories does matter) e.g. BMI (underweight, normal, overweight, obese) e.g. Pain severity (None, Mild, Moderate, Severe)

• Numerical

– Discrete (counts)

e.g. Number of hospital admissions.

– Continuous (measurements) e.g. Weight, Height, BMI, blood pressure, cholesterol, etc

[pic 2]

CATEGORICAL NUMERICAL

-BMI Category is ordinal (order does matter/logical)

-Cancer stage is ordinal

-Number of hospital admission is discrete

BMI can be continuous (calculated by weight (kg)/[height (m)]^2 or be categorised such as underweight, healthy weight, overweight, obese, morbidly obese

In which is transformation possible/not possible? – Continuous -> categorical can be transformed, but cannot be transformed from categorical -> continuous.

Population vs Sample

- The population: The entire collection of patients of interest, as defined in the study design (e.g. all diabetics in Australia

- Actual population of interest is large hence the solution is to take a sample that is representative of part of the population of interest (e.g. sample of 100 diabetics in Australia)- this data is used to make inferences about the population of interest

Sampling

- Sampling is normally at the individual level otherwise when investigating an intervention it is at a ‘cluster’ level (e.g. house, school classrooms, schools, hospitals)

TYPES OF SAMPLES – PROBABILITY SAMPLING

- Simple random sample (SRS) – need list of population, then take a random sample

[pic 3]

- Systematic sample – good for production lines (e.g. Take every 20th item)

[pic 4]

- Stratified sample – similar to SRS, more than one group (e.g. F/M)

[pic 5]

- Cluster sample – either sample everyone in the small cluster, randomization at cluster level not person

[pic 6]

TYPES OF SAMPLES – NON-PROBABILITY SAMPLING

- Quota or convenience sampling – continue sampling people until you have the correct number [pic 7]

- Purposive sampling – Sample a particular subset of people, reject those not suitable (e.g. non-working mothers, ask mother if they work first)

- Judgment sampling – used for a quick sample (e.g. Journalist asks people on the street for views, may be biased)

- Snowball sampling – find people who meet the criteria, ask them to refer you to others (e.g. The Burnet Institute recruit people for many studies where there is no ‘list’ of eligible people, for example injecting drug users (IDU) and Men who have Sex with Men (MSM). Another example is viral social media, sharing on Facebook and appearing on newsfeed

LECTURE 3

LEARNING OBJECTIVES

- Identify the public health definition of injury and its classification

- An understanding of descriptive epidemiological concepts and terms

- Ability to distinguish between mortality and morbidity

- An understanding of DALYs and QUALYs

Focus is on accident prevention rather than injury prevention – If you could understand what led to the accident then you could intervene to that circumstance and reduce future events

DOMINO THEORY

- Accidents were the result of a chain of events

- An event for which no one, except the victim was responsible

CURRENT PUBLIC HEALTH THINKING

- Injuries occur as the result of energy transfer that is delivered in excess of a threshold

- Types of energy that can cause injury:

- Electric

- Chemical

- Mechanical

- Thermal

- Radiation

Moor vehicle crash – Mechanical

Lacerations/cut – Mechanical

Poisoning – Chemical

Drowning – Mechanical

Falls – Mechanical

Gunshot Wound – Mechanical

Concussion – Mechanical

Burns from Fires – Thermal

Electrocution – Electric

INJURY PREVENTION

- Injury prevention like all prevention starts in the same place

- The first stage of epidemiologic investigation, it focuses on describing disease distribution by characteristics relating to time, place and person

- Who, what, where, when

- Descriptive Epidemiology

POPULATION AT RISK

- Population at risk should only include people who are potentially susceptible to the disease being studied

- Population at risk can be defined by demographic, geographic or environmental factors

- E.g. Occupational injuries = workforce

MORTALITY

- Counting the number of deaths

- Common measure

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