poph3000: population health (wk1-wk6)

major categories of risk factors: BEINGS model

(B) biological and behavioural: - tissue, organs, cells, age, sex, weight - physical inactivity, unhealthy diet, unprotected sexual intercourse, shared intravenous drug use (E) environmental: - may include temperature, food, pollutants, population density, sound, light, and parasites - e.g.- Legionella pneumophila (Legionnaires disease) thrives in poorly maintained air-conditioning cooling towers (I) immunological: - individual immunity leads to herd immunity against biological agents - e.g.- smallpox eradication (N) nutritional: - poor dietary intake (e.g.- eating few meals; consuming limited portions of fruits, vegetables, or milk products; and experiencing an unintentional weight change) - e.g.- nutritional differences (larger consumption of fish, vegetables and fruit) between Japanese Americans and Japanese in Japan explained rates of myocardial infarction (MI) - e.g.- Denis Burkitt discovered cross-cultural differences in disease occurrence due to dietary fibre intake (e.g.- CVD and diabetes in western countries) (G) genetics: - genetic epidemiology: > interaction of genetic inheritance and environment > particular focus on the impact of gene mutation > increase in genetic diseases attributed to increased identification and decline in non-inheritable disease - genetic screening: > DNA testing > e.g.- BRCA1 and BRCA2 mutations for breast and ovarian cancer (Angelina Jolie) (S) services, social and spiritual: - services: > medical treatment and eradication of disease but can also be a risk > iatrogenic disease: * tissue or organ damage that is caused by necessary medical treatment, pharmacotherapy, or the application of medical devices and has nothing to do with the primary disease * e.g.- side effects of a drug intervention (e.g.- serotonin-norepinephrine reuptake inhibitors (SNRIs), in treating depression can increase anxiety) or drug interaction * e.g.- infection from surgical intervention * e.g.- hospital superbugs (e.g.- MRSA (methicillin-resistant Staphylococcus aureus)) - social and spiritual support: > can reduce susceptibility to disease > may improve recovery > assists in disease risk and progression

Ross, et al. (1999) 'Clinical Outcomes in Statin Treatment Trials'

(see 'Diamond & Ravnskov, 2015) - comments on people trying to show off the efficacy of statin treatments in the prevention of cardiovascular disease - drug companies and some researchers maximise the supposed benefits of statin interventions and minimise the risks associated with the drug (by selectively highlighting specific statistical data) - many compare the Absolute (ARR) and Relative Risk Reduction (RRR) - statins may lower cholesterol levels in the arteries by blocking the production of cholesterol reactant substances - statins are highly effective and safe for most people, but they have been linked to muscle pain, digestive problems and mental fuzziness - some studies will not compare the same outcomes (and thus have different comparable outcomes, sample sizes, etc.) - when the odds ratio is bouncing between <1 and >1, there is no certainty that the drug is working - ratios can be written in different ways '1 fatal myocardial infarction treated per 166 people' looks better when written '602 fatal myocardial infarction treated per 100,000 people

Diamond & Ravnskov (2015) 'How statistical deception created the appearance that statins are safe and effective in primary and secondary prevention of cardiovascular disease'

(see 'Ross, et al. (1999)') - statins may lower cholesterol levels in the arteries by blocking the production of cholesterol reactant substances - statins are not very effective in people who are not symptomatic or have selected risk factors for myocardial infarction/ strokes - explain the differences between ARR and RRR and their manipulation - the RRR can inflect the data - studies discussed: Jupiter Trial, ASCOT-LLA, British Protection Study - the "framing effect" is a cognitive bias where people decide on options based on whether the options are presented with positive or negative connotations; - people tend to avoid risk when a positive frame is presented but seek risks when a negative frame is presented - long-term effects of the drugs (e.g.- liver damage, cancer, mental illness) are not reported due to the short-term nature of these studies - many drug companies provide research funds for research on drugs that THEY ARE SELLING!

measures of health status

(see 'measures to assess public health') - mortality rates (and birth rates) - life expectancy - disability-adjusted life years (DALYS) - health index & health profiles - quality of adjusted life years (QALYs) - CDC Health-Related Quality of Life Index: > general health > activity limitations > number of days with health symptoms

behavioural factors in health promotion

(see 'social dimension to health prevention') - improvements in behaviour can have tremendous health impacts - Five A's Model for Smoking Cessation: 1. Ask about smoking every visit 2. Advise all smokers to quit 3. Assess smokers' willingness to quit 4. Assist smoker in attempts to quit (e.g.- nicotine patches) 5. Arrange follow-up contact

burden of injury

- "burden of disease": > total significance of disease for society beyond the immediate cost of treatment > measured in years of life lost to ill-health as the difference between life expectancy and disability adjusted life expectancy - "burden of injury": > behavioural and biological characteristics (BEINGS model) in some individuals make them more impulsive and reckless > namely, teenagers and young adults are high risk takers > unintentional injuries are leading causes of death and disability amongst teenagers and young adults > e.g.- reduced seat belt use rates correlate with vehicular fatality rates

public health

- "is what we, as a society, do collectively to assure the conditions in which people can be healthy" - promote health and prevent disease - focused on: > health status of the public and > efforts to preserve and improve population health - 3 main responsibilities: > assessment > police development > assurance of available and appropriate services

evaluation of public health interventions

- 'public health intervention' is an organized effort/ policy to promote specific behaviors and habits that can improve health - e.g.- National Cervical Screening Program (NCSP), HPV vaccine, water fluoridation, skin cancer prevention, plain tobacco packaging, road safety, National Firearms Agreement - an evaluation provides findings to enable immediate, data-driven decisions for improving program delivery - a noticeable decline in evaluative data (with stagnant low prevalence numbers) indicate that the public health intervention has had positive long-term effects (e.g.- the oral polio vaccine)

difference between ARR and RRR

- ARR represents the difference in event rates (e.g.- disease-related) between the experimental group and the control group - RRR is a proportional measure estimating the size of the effect of a treatment compared with other interventions or no treatment at all

theories of behaviour change

- Health Belief Model (the individual must believe that their disease is serious, they are at risk due to the disease, the preventative measure is effective, there are no side effects related to the measure) - Transtheoretical Model (dynamic process of thinking; involves a precontemplation, contemplation, preparation, action, etc. stage) - Theory of Planned Behaviour and Reasoned Action (behavioural intentions are the most important factors in improving health; the more individuals believe they have control over their behaviour, the more likely they will be to change their behaviours) - Precaution Adoption Process Model (behaviour changes when they become aware of a health issue; transition through a stage of ignorance, then action, then maintenance) - Social Learning Theory (individuals model their behaviour based on others in their environment) - these models address common assumptions: > whilst knowledge about prevention and disease is necessary, knowledge is not sufficient for behaviour change > health behaviour is affected by what people know, but also how they think > health behaviour is influenced by their perception of their own behaviour, risks, motivations and environment

criteria for causality

- Mills proposed five criteria for causality: 1. strength: > substantial size of an association in which those with a risk factor are more likely to report some outcome over those without the risk factor > statistical difference between those with and without the risk factor AND those who have a likelihood of reporting a disease → with a substantive significance! 2. consistency: > how consistent are the findings? > reliability of the research findings > imperative to undertake replications of the findings within other samples and contexts to ensure that the identified risk factor is consistently reported > i.e.- repeated observation of an association in different populations under different circumstances > however, lack of consistency does not rule out a causal association, because some effects are produced by their causes only under unusual circumstances, or specific samples 3. specificity: > the absence of a risk factor results in no difference in the likelihood of reporting a disease > requires that a cause leads to the exact, single effect studied (and not multiple effects) > e.g.- smoking has several effects on the smoker 4. biological plausibility: > a biological pathway between risk factor and disease > biological basis for the progression of the risk factor causing or initiating the cause to the disease in in question 5. dose-response: > risk for disease increases with greater exposure to the risk factor > 'biological gradient' refers to the presence of a monotone (unidirectional) dose-response curve > e.g.- more smoking → more carcinogen exposure → more tissue damage → more carcinogenesis > a J-shaped dose- response curve is at least biologically plausible *** - temporal relationship: > necessity that the cause precedes the effect in time > i.e.- the risk factor must occur first in order to produce the outcome - elimination of other explanations: > alternative explanations that may account for the disease > take home message: hypotheses alter according to the paradigm shifts of science > e.g.- rates of malaria drop when swamps are drained → alternative explanation: mosquito numbers fall well swamps are drained > e.g.- peptic ulcers were once thought to be the result of poor diet, obesity and increased alcohol consumption → Helicobacter pylori was responsible (but at this time, it was believed that bacteria could not survive the pH of the stomach)

numbers needed to harm (NNH)

- NNH is the number of patients who would need to be treated in order for one patient to benefit from the treatment - if NNH = 1, the drug was harmful for every person treated - if NNH = 2, the drug was harmful to every second person treated - if NNH = 100, you would need to treat 100 people in order for the drug to be harmful to one person - NNH = 1/ARI - ARI = AAR (when the treatment group report greater numbers of events) - ARI is always a positive integer

numbers needed to treat (NNT)

- NNT refers to the number of patients who would need to be treated in order for one patient to benefit from the treatment - i.e.- the practical value of the intervention - if NNT = 1, the drug was of benefit for every person treated - if NNT = 2, the drug was of benefit for every second person treated - if NNT = 100, you would need to treat 100 people in order for the drug to show benefit to one person - NNT = 1/ ARR = 1/ 0.1* = 10 ∴ you would need to treat 10 people in order for the drug to show benefit to one person *(see 'uses of risk assessment data example' for the specific example)

Rao (2020) 'Medical certification of cause of death for COVID-19'

- Week 1 - searchable by CTRL-F

Victoria: Department of Health (2011) 'Guidance Note for the Verification of Death'

- Week 1 - searchable by CTRL-F

WHO (2020) 'International Guidelines for Certification and Classification (Coding) of COVID-19 as Cause of Death'

- Week 1 - searchable by CTRL-F

AIHW 'Population-level prevention initiatives and interventions'

- Week 2 - searchable by CTRL-F

Lokuge, et al. (2015) 'Successful Control of Ebola Virus Disease: Analysis of Service Based Data from Rural Sierra Leone'

- Week 2 - searchable by CTRL-F

Miller, et al. (2004) 'Evaluation of Australia's National Notifiable Disease Surveillance System'

- Week 2 - searchable by CTRL-F

Christensen, et al. (2016) 'Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): a randomised controlled trial'

- Week 3 - searchable by CTRL-F

Diamond & Ravnskov (2015) 'How statistical deception created the appearance that statins are safe and effective in primary and secondary prevention of cardiovascular disease'

- Week 4 - see analysis card above - searchable by CTRL-F

Ross, et al. (2016) 'Clinical Outcomes in Statin Treatment Trials'

- Week 4 - see analysis card above - searchable by CTRL-F

Uplekar, et al. (2015) 'WHO's new End TB Strategy'

- Week 5 - searchable by CTRL-F

Patterson & Chambers (1995) 'Preventive health care'

- Week 6 - searchable by CTRL-F

natural history of disease (1)

- aetiology follows the progression of a disease is referred to as the "natural history" of disease - the main purpose of population health interventions is to alter this natural history, either through prevention or treatment - stages of disease: stage of susceptibility → exposure → pathological changes → stage of subclinical disease → onset of symptoms → stage of clinical disease → usual time of diagnosis → stage of recovery, disability or death

rehabilitation

- after disease has caused damage - strengthen remaining functions - learn other functional capacities - e.g.- navigating in a wheelchair, learning how to speak again, using a prosthetic - demanding on patient - involves several specialists - may require psychological assistance - categories of disability: > permanent total disability (e.g.- severed spinal cord) > permanent partial disability (e.g.- amputations, back injury) > temporary total disability (e.g.- induced coma, car accident) > temporary partial disability (e.g.- breaking a leg)

causal and non-causal associations

- association: > a basic requirement for a causal relationship > some relationship must exist > e.g.- factor A ↔ factor B > knowing something about factor A, automatically means you know something about factor B, because they are correlated - correlation: > e.g.- imagine playing a piano: * if both your hands play the same notes, in the same direction (but on different parts of the piano), that is called "positive correlation" → both factors are either going up or down * if both hands start at the same key and the left hand plays all the keys on the left-hand-side, whereas the right hand plays the keys on the right side of the original key (i.e.- they are playing in opposite directions), that is called "negative correlation" → one factor goes up, the other goes down > with Venn diagrams, the more overlapping the circles are, the more likely your estimation of details about the other variable will be true > with Venn diagrams, if neither variables' circles overlap, they do not have anything in common > HOWEVER, you should not think that factor A is 'CAUSING' factor B (with this information alone) → they are simply correlated > spurious associations: * when events are associated but not causally related, due to coincidence or the presence of a certain third, unseen factor * e.g.- divorce rates in Maine correlated with per capita consumption of margarine (US) * e.g.- US crude oil imports from Norway correlated with drivers killed in collision with railway train * e.g.- US spending on science, space and technology correlates with suicides by hanging, strangulation and suffocation - direct causal association: > one factor predicts (or causes) another > this is called a "regression" > e.g.- factor A → factor B - indirect causal association: > intervening/ mediating variables ("mediators") > e.g.- * factor M → factor B * factor A → factor M * factor A → factor B * here, factor A directly causes M, and therefore indirectly causes B * factor M is mediating the effects of factor A on factor B * since factor A can still cause a direct effect on factor B, this is called "partial mediation" * if factor A → factor M → factor B (without factor A → factor B also), this is called "full mediation" - moderator: > e.g.- factor A → factor B BUT factor M can ↓ on the relationship > factor A does not predict/ cause factor M (the moderator) > factor M is an additional, third-party variable, which influences the relationship between A and B > e.g.- sunlight in photosynthesis (it's on the arrow, not really a part of the equation) - bi-directional causation: > e.g.- factor A → factor B → factor A > e.g.- increased availability → increased consumption → increased disease

risks example: smoking

- assumptions: > lung cancer deaths in smoker: 191 per 100000 per year > lung cancer deaths in non-smokers: 8.7 per 100000 per year > proportion exposed (population of smokers averaged over time): 35% or 0.35 > proportion risk of lung cancer: 72.5 per 100000 per year - Attributable Risk (AR) = risk (exposed) − risk (unexposed) = 191 − 8.7 = 182.3 per 100000 - Relative Risk (RR) = risk (exposed)/ risk (unexposed) = 191/ 8.7 = 21.95 ≈ 22 (to the nearest whole number) - AR as a % = [(risk (exposed) − risk (unexposed))/ risk (exposed)] X 100 = [(191 − 8.7) 191] X 100 = 0.954 = 95.4 % OR! OR! OR! AR as a % = [RR^(−1)/ RR] X 100 = [22^(−1)/ 22] X 100 = 0.954 X 100 = 95.4 % - Population Attributable Risk (PAR) = risk (total) − risk (unexposed) = 72.5 − 8.7 = 63.8 per 100000 - PAR as a % = [(risk (total) − risk (unexposed))/ risk (total)] X 100 = [(72.5− 8.7)/ 72.5] X 100 = (63.8 72.5) X 100 = 0.88 X 100 = 88% OR! OR! OR! - PAR as a % = [((*Pe)(RR^(−1)))/ (1 + (Pe)(RR^(−1)))] X 100 = (0.35)(22^(−1))/ (1 + (0.35)(22^(−1)) X 100 = 7.35/ (1 + 7.35) X 100 = 0.88 X 100 = 88% *Pe = proportion of exposed population

common pitfalls in assumptions of research findings

- bias/ differential error: > effects: * influences understanding by weakening the true association * potentially producing a false association * distorting the direction of an effect > similar to the issue of confounding variables > assembly bias (attributed to the participants of the study): * selection bias: . biases when participants are elected for the study . e.g.- seriously ill (want to try new treatments) . random selection is preferred over experimental or intervention groups * allocation bias: can occur when the participants are not elected at random * validity: . internal (assumed upon appropriate study design)/ external validity (how well it can be generalised to broader populace) . generalisability (the extent to which the findings of a study can be applicable to other settings/ populations → representative studies ✓) > detection bias (failure to detect or identify a case/ factor): * measurement bias (incorrect/ inappropriate data collection → e.g.- participants overestimate their height and underestimate their weight) * recall bias (findings can be impacted by poor recollection of events) - random error (inherently unpredictable fluctuations in the readings of a measurement apparatus?) - confounding (see 'statistical significance: confounding factors') - synergism (combined effect of two risk factors are greater than the individual effects → e.g.- asbestos + smoking is a greater risk than each on their own) - effect modification (multiplicative effect; the effect is not the same for all levels of the second risk factor → e.g.- age VS hypertension is not the same for men and women) - interaction effect (e.g.- men have a higher risk for hypertension in mid-life, women have a higher risk for hypertension in later life)

mechanisms/ causes of disease

- biological mechanisms - social causes - behavioural causes - environmental causes

screening tests and immunisation guidelines (for men 50-64)

- blood pressure test (at least every 2 years) - blood A1c test [diabetes] (every 3 years) - colonoscopy (every 10 years) - hearing test (every 3 years) - mole exam (monthly mole self-exam) - influenza vaccine (once a year) - tetanus-diphtheria booster vaccine (every 10 years) - dental exam (routinely)

changes in demography and role of diseases

- changes in the burden of disease over time - compare the decline in rates of heart disease and stroke with changes in cancer

categories of risk indicators

- compare risk of disease between groups: > 'Attributable Risk' or AR indicator assesses difference in risk between groups > 'Risk Ratio' or RR assesses relative risk between groups (ratio of the risk in an exposed group to the risk in the unexposed group) > 'Odds Ratio' or OR reflects the odds or likelihood of an event/disease. - determine the risk associated with particular risk factors: > AR is a % of those exposed (an extension of the Attributable Risk as a proportion of those who were exposed) > 'Population Attributable Risk' or PAR is a measure of how much of the total risk for a disease is attributable to a single independent variable > PAR as a risk percent is the PAR converted into a percentage of the PAR (?)

Burns, et al. (2012), 'Positive components of mental health provide significant protection against likelihood of falling in older women over a 13-year period', International Psychogeriatrics

- context: > in late life, falls are associated with disability, increased health service utilization and mortality > risk factors of falls include falls history, grip strength, sedative use, stroke, cognitive impairment, and mental ill-health - results: > vitality (i.e.- positive well-being) has significant protective effects on the likelihood of falls > in comparison with mental health, vitality reported much stronger protective effects on the likelihood to fall (in comparison with the risk of poor mental health) > both physical health and mental health account for much of the variance in vitality > interpreted via an 'odds ratio' (OR) > OR: measure of association between an exposure and an outcome > if OR < 1, this factor reduces the likelihood of receiving the outcome (e.g.- OR = 0.5 → 50% less likely) > if OR = 1, risk factor (or "predictor variable") has no outcome on the area of interest > if OR = 1.5, if you have the risk factor, you are 1.5X more likely to receive the outcome than someone who does not have the risk factor (50% increase in likelihood) > if OR = 2, you are twice as likely to have the outcome than someone who does not have the risk factor (100% increase in likelihood)

CDC Health-Related Quality of Life Index

- core health days measures: 1. Would you say that in general your health is excellent, very good, good, fair, or poor? 2. Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? 3. Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good? 4. During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation? - activity limitations module: 1. Are you LIMITED in any way in any activities because of any impairment or health problem? 2. What is the MAJOR impairment or health problem that limits your activities? 3. For HOW LONG have your activities been limited because of your major impairment or health problem? 4. Because of any impairment or health problem, do you need the help of other persons in handling your ROUTINE needs, such as everyday household chores, doing necessary business, shopping, or getting around for other purposes? 5. Because of any impairment or health problem, do you need the help of other persons with your PERSONAL CARE needs, such as eating, bathing, dressing, or getting around the house? ↓ - options: > arthritis/ rheumatism > back or neck problem > fractures, bone/ joint injury > walking problem > lung/breathing problem > hearing problem > eye/ vision problem > heart problem > stroke problem > hypertension/ high blood pressure > diabetes > cancer > depression/ anxiety/ emotional problem > other impairment/ problem - healthy days symptoms module: 1. During the past 30 days, for about how many days did PAIN make it hard for you to do your usual activities, such as self-care, work, or recreation? 2. During the past 30 days, for about how many days have you felt SAD, BLUE, or DEPRESSED? 3. During the past 30 days, for about how many days have you felt WORRIED, TENSE, or ANXIOUS? 4. During the past 30 days, for about how many days have you felt you did NOT get ENOUGH REST or SLEEP? 5. During the past 30 days, for about how many days have you felt VERY HEALTHY AND FULL OF ENERGY?

economics of prevention

- cost-benefit analysis: measures costs VS benefits of a prevention strategy - cost-effectiveness analysis: > compares different prevention strategies > must consider inflation and depreciation of the dollar > discounting - future cost = (1 + r)^n, where r = yearly interest rate (0.05%) and n is the number of years of benefit > e.g.- treatment costs $1000, with annual 5% inflation over a 20 year period = (1 + 0.05)^20 = 2.653 X $1000 = $2653

direct standardisation VS crude rates

- crude rates are calculated based on the population under study as a whole - standardized rates are based on particular characteristic(s) as standard

public health data and monitoring

- data collection informs public health policy - international organisations (e.g.- World Bank, WHO, OECD) - comparison can be made between nations - rely on quality data collection by member states - increasing reliance on electronic data - public health surveillance - census data - registration systems (e.g.- NSW Registry of Births Deaths & Marriages) - geographic information systems (e.g.- geo-mapping, Twitter)

issues of research design in epidemiology

- data dredging and 'p'-values: > observational studies, despite the fact that they have larger, representative sample sizes and easier claims to external validity > one issue here, when the sample size is very large, a statistically significant reporting can be found, even when the magnitude/ size of the effect is small > this is why it is important to consider the substantive significance (instead of simply the statistical significance) > in the case where 10 variables are being tested in a large observational study, 45 different tests of association > however, 5% of the 45 tests (2.5 tests) will have a (statistically) 'significant' value of <0.05 by pure chance > it is possible that all the tests ARE statistically significant, and it is also possible that none of the tests are significant > solution: repeat, repeat, repeat! replicate! replicate! replicate! > the multiple statistical significances could be caused by random error or bias - ethics: > research activities are undertaken under the auspices of various ethical review boards > this includes research involving humans, animals, plants and even non-organic materials (due to possible inadvertent effects on humans and the environment) > some social and psychological researchers do not follow modern ethical law

role of epidemiologists: surveillance and community responsibilities

- diagnosing the disease progression - determining effective treatments and prognosis (disease outcome) - monitoring of 'field trials' to evaluate the effectiveness of community vaccination programmes - continual surveillance of interventions to monitor safety - bioterrorism (and the monitoring of threat from disease occurrence) - identifying key population health threats (e.g.- HIV in the 1980s; SARS in early 21st Century) - estimating population risk

reducing health disparities (life expectancy VS sex)

- difference in life expectancy between: > white female > black female > white male > black male - females have higher life expectancy than men, however, these statistics are very slowly equating (e.g.- black female and white male life expectancies are similar) - black males have the lowest life expectancies - white females have the highest life expectancies

tertiary prevention

- disability limitation: > halting disease progress > limiting injury damage > strategies of symptomatic stage prevention * modifying diet, behaviour & environment * screening for complications * treating complications - rehabilitation: > reducing physical functionality of disability > reducing social and psychological impacts of disability

minimum requirements for community screening

- disease requirements (even if any one of these is not even PARTIALLY met, there the appropriateness and cost benefit of community screening is questioned): > disease must be serious/ with severe burden > an effective therapy > natural history of disease is understood (to understand the efficacy of the intervention at each stage) > disease/ condition must not be too rare (may lead to a false positive) or too common - screening test requirements: > quick (reduces waiting time), easy and inexpensive > safe and acceptable (objections to genetic screenings, colonoscopies, etc.) > sensitivity (false-negative)/ specificity (false-positive)/ positive prediction - healthcare system requirements: > positive results lead to follow-up > treatment should be available (it is unethical to inform someone of a disease they have, without giving them sufficient access to the appropriate therapy) > treatment should be acceptable and ethical > screened population should be well-defined (by qualified practitioners)

vaccine prevention

- elimination of disease globally (e.g.- polio) - elimination of disease in particular regions (e.g.- polio exists in Pakistan and Afghanistan) - displays efforts in reducing disability and mortality related to that disease - intact immunity: > 'immunity' is adequate immuno-defences to fight a biological invasion > 'intact immunity' is the innate immunity at birth - passive immunisation: > shorter in duration (in comparison to active immunity) > at birth, maternal antibodies provide passive immunity (via. placenta, breast milk) - types of immunity: > passive immunity > maternal antibodies > individual/ herd immunity (when large portions of the population are immunised and can provide protection for non-immunised individuals) - types of vaccines: > inactivated (killing microbe with heat, chemicals or radiation; stable, safer, no refrigeration required, longer shelf-life, accessible to people in developing countries) > life-attenuated > toxoid vaccines (bacterial toxins inactivated by formaldehyde; e.g.- diphtheria vaccines) - immunisation schedules: > important because we lose immunity as we reach adulthood > all adults should be vaccinated for annual influenza, measles, mumps, rubella, varicella and human papillomavirus > not necessary for the elderly to have the measles, mumps, rubella vaccine > between 15-19 years, teens should get a meningococcal vaccine > all indigenous adults should get a hepatitis B shot > at risk patients (medical, behavioural, occupational) should get more vaccines

ecology: ecological perspective of disease

- emphasises people exist in a community - complex interaction of the individual, social contexts and environment - important to understand this relationship and its impact on factors underlying disease processes in order to improve population health

investigation of epidemics

- epidemic: > pattern or occurrence of disease that is atypical > widespread occurrence of an infectious disease in a community at a particular time > can be an outbreak/ occurrence that stems from baseline disease patterns > regular occurrence of a disease is called its 'endemic level' - attack rate: > proportion of exposed persons that become ill > (# new cases/ # persons exposed over relatively short period) > increasing attack rate can be used to argue for an impending epidemic

variation of disease patterns

- establishment of baseline rates - by comparing departures from baseline norms, epidemiologists can conclude an atypical disease outbreak, and identify the potential causes - trends over time - public health involves the long-term data analysation of specific trends - methods can include repeated national surveys about particular diseases to obtain information about their prevalence - must be able to interpret different scales (i.e.- the y/x axis of a graph may not have equivalent breaks) - seasonal variation: > e.g.- seasonal variation (more human behaviour) of chickenpox) > should take into account the differences between the upper/ lower hemispheres, additional seasons in some locations, etc. > sometimes the prevalence of a disease is dependent on the prevalence of their vectors (which can be dependent on the season) > e.g.- dengue fever VS monthly rainfall; malaria VS yearly rainfall

uses of risk assessment data

- estimating benefit of intervention with PAR: > PAR compares the efficacy of an intervention by adjusting the total risk for the average of the disease rates in exposed VS unexposed > rate per 100,00 male population per year = (weight of smokers)(rate of smokers) + (weight of non-smokers)(rate of non-smokers) = (0.10)(191) + (0.90)(8.7) = 19.1 + 7.8 = 26.9 (per 100,000) PAR = 26.9 - 8.7 (lung cancer deaths in non-smokers) = 18.2 (per 100,000) ∴ PAR with smoking rates of 25% - PAR with smoking rates of 10% = 45.6* - 18.2 = 27.4 (per 100,000) ∴ 27.4 cancer deaths per 100,000 men per year could be prevented *where did this number come from - estimating financial costs and benefits in health outcomes (benefits of treatments VS economic value) - Relative vs. Absolute Risk: > Absolute Risk Reduction (ARR) = risk (exposed) - risk (unexposed) > Relative Risk Reduction (RRR) = (risk (exposed) - risk (unexposed))/ risk (exposed) [the are the same formulas as AR and RR, but instead for the risk of the intervention, not the disease itself]

rate VS risk

- evaluating targets: > 50% reduction of anaemia in women of reproductive age > 30% reduction in low birth weight > no increase in childhood overweight - population comparisons: > between nations (cross-national) > within nations, but between groups > e.g.- rates of suicide within different age groups within Australia > e.g.- efficacy of a health intervention (experimental group VS control group) - temporal comparisons: > changes within populations over time (repeated cross-sectional) > e.g.- National Survey of Mental Health and Wellbeing are conducted annually ("longitudinal", however, since they do not follow the same individuals over time, they are actually cross-sectional) > changes within the same individuals over time (truly longitudinal) > the ANU's 'Personality & Total Health (PATH) Through Life', which has 7500 people aged 20, 40 and 60, checking on them every four years (currently in 16th year)

2X2 risk factor table example

- facts: > 40 people have the risk factor and have the disease > 10 people have the risk factor but do not have the disease > 5 people do not have the risk factor but have the disease > 45 people do not have the risk factor and do not have the disease - a = risk factor positive (disease present) = 40 - b = risk factor positive (disease absent) = 10 - c = risk factor negative (disease present) = 5 - d = risk factor negative (disease absent) = 45 - you can calculate (a + b) and (c + d) [horizontal rows] - you can calculate (a + c) and (b + d) [vertical rows] - (a + b + c +d) - finding all the risks! > AR = [a / (a + b)] - [c / (c + d)] = [40 / (40 + 10)] - [5 / (5 + 45)] = [40 / 50] - [5 / 50] = 0.8 - 0.1 = 0.7 ∴ 70% of the disease cases are attributable to the risk factor > RR = [a / (a + b)] / [c / (c + d)] = [40 / (40 + 10)] / [5 / (5 + 45)] = [40 / 50] / [5 / 50] = 0.8 / 0.1 = 8 ∴ the risk of having the disease in in the positive risk factor groups is 8 times greater than the negative risk factor group (ratio between the exposed and unexposed groups) > OR = (ad)/ (bc) = (40*45)/ (10*5) = 1800/50 = 36 ∴ you are 36 times more likely to have the disease if you have the risk factor than if you did not have the risk factor (describes the odds of those who have the risk factor in comparison to those without the risk factor )

issues with screening programmes

- false-positive screening results: > can cause undue concern/ stress > e.g.- 49% of women who have had 10 mammograms can expect to have at least one positive reading (error rate of 6-7% each mammogram) - false-negative screening results: > can be disastrous > individuals may delay seeking further medical treatment due to receiving the all-clear - bias: > selection bias (those who freely elect-to-participate are more likely to be health conscious; people may believe that because of their family history, they are more prone to a disease) > lead-time bias: * lead-time is the period between moment of diagnosis and disease outcome (death or recovery) * people with slower-to-develop disease (e.g.- slow growing cancers) are more likely to be detected community screening * people with aggressive, disabling diseases are more likely to be detected in a primary care setting > length bias - multiphasic screening ('p'-value appear when it's actually just a false positive)

epidemiology: classical

- focuses on discovering risk factors that might prevent or delay disease, injury or death within a population - with a focus on community - risk factors: * infectious disease (e.g.- HIV, bird flu) * nutrition (e.g.- obesity, malnourishment) * environment (e.g.- pollution, carbon monoxide) * social contexts (e.g.- war, universal health care protection, equality) * behaviour (e.g.- sedentary lifestyle, alcohol, smoking, drug consumption)

tertiary prevention example 2: hypertension

- force of the blood against the artery walls is too high - results elevated blood pressure - significant risk factor for for heart attack, stroke, chronic kidney disease and chronic heart failure - strong association with age (argument to change 'normal BP' in older adults) - note: hypotension, although more common in hospital settings (e.g.- major blood loss) is not as commonly reported - terms: > systolic: pressure when the heart pumps/ contracts > diastolic: pressure when the heart rests between beats - guidelines: > systolic (<120 mm Hg) + diastolic (<80 mm Hg) → normal BP → no need for drugs > systolic (120-139 mm Hg) + diastolic (80-99 mm Hg) → prehypertension → may need drugs, monitor > systolic (140-159 mm Hg) + diastolic (90-99 mm Hg) → stage 1 hypertension → drugs needed; thiazides usually > systolic (≥160 mm Hg) + diastolic (≥100 mm Hg) → stage 2 hypertension → drugs needed; two-drug combination

randomised clinical trials (RCT)

- gold standard for assessing interventions - however, several RCTs must be conducted to ensure that the data is generalisable - in contrast, in observational studies, it is easier to derive conclusions from much larger representative samples (i.e.- claim external validity) - traits: > intervention(s) vs. control group (no treatment, placebo, treatment as usual) > single blind (participants do not know if they are in the intervention or placebo group) > double blind (BOTH participants and investigators do not know who is in the intervention or placebo group) > triple blind (participants, investigators AND data analysts do not know who is in the intervention or placebo group) > intention to treat (other factors that influence the efficacy of the tested variable) - process: > recruit → randomise > to a specific group, administer experimental intervention > to the remaining group, administer the placebo or other non-experimental intervention > measure outcomes for both - e.g.- "Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): A randomised controlled trial", Christensen, et al. (2016)

surveillance of disease

- identifies the occurrence of a disease outbreak - pertinent to effective disease control - management of surveillance activities: > state and territory responsibilities (primary) > communicate information to Commonwealth and other states > Federal government has responsibility to control outbreaks that may affect the nation or multiple states > Federal government is also responsible for national quarantine and international reporting > Australia's Department of Health Report (https://www1.health.gov.au/internet/main/publishing.nsf/Content/cda-surveil-surv_sys.htm) - types: > passive: * routine reporting of diseases from physicians, clinicians, laboratories and hospitals * typically compiled on forms and/ or entered in online databases > active: * labour-intensive periodic reporting * data-analysts actively seek out, contact and gather information from healthcare providers

measures of frequency of disease

- incident cases: > number of incidents (from being well to ill) during the period of the study > e.g.- number of new cases of a particular disease each year > 'cumulative incidence' is the number of events that newly occur in the population over a specified period > in a t1 → t2 graph, it is the number of NEW DOTS occuring within the time frame, whether they have been resolved or not > 'new dots' suggests they have not carried on from before t1 - point prevalence: > proportion of people with a disease at a given moment of time > in a t1 → t2 graph, observe all the provided dates > 'point prevalence' refers to all the cases active during that exact date (i.e.- see what lines run through the date) - period prevalence: > number of people with a disease over a specific period of time > does not matter if the cases have not been resolved yet or resolved an hour after t1 > every single case between t1 and t2 - prevalence = incidence X average duration - means that fewer people are catching the disease, but those with the disease are living with that disease for longer (e.g.- AIDS)

epidemiology: infectious VS chronic

- infectious disease epidemiology: > focused on contagious diseases and which is heavily dependent on lab work > e.g.- West Nile virus ? - chronic disease epidemiology: > focused on diseases which are more often an outcome of social-environmental factors > e.g.- * sedentary lifestyle increases risk for CVD * smoking increases risk for lung cancer - some diseases (e.g. TB, HIV) are considered both chronic and infectious

rates of disease

- is the number of events that occur in a defined time period, divided by the average number of people at risk for the event during the period under study - rate of disease = (# of events/ average total population) - constant multiplier: > X100 > X1000 > X1000000 - example: 3 nursing homes with 100 elderly women in each; 10 deaths in each home > # of events/ AVERAGE total population > 10/ [(100 + *90)/ 2] = 0.105 = 10.5% > *90 because women died in each home > you need to use a constant multiplier to remove the decimal place (X1000) > 10.5% X 1000 = 105 > this means 105 deaths every 1000 people, per year > sometimes, you will need to use large constant multipliers (in context) for easier comparison > e.g.- if there are 40,000 older women in that location, it would make more sense to use a X10,000 multiplier - 'crude death rate': mid-period death and population - 'cause-specific death rate': reflects proportion of deaths attributable to a particular disease, divided by the mid-period population - it is important to remember that incidents of death do not occur in a linear trajectory - types: > incidence rates: * equivalent to the mortality rate * = number of incident cases/ mid-population at risk during study period * (see nursing home example) → 10 deaths/ 95 people alive during mid-point of study = 105 in 1000 deaths > incidence density: * when multiple events (of the same nature) occur several times over a set period * e.g.- 3 patients reported 5 ear infections over 50 person-months > prevalence rates: proportion of a sample who report a disease, risk factor or some other condition - categories of rates: > crude: * rough average of some population characteristic * calculated from specific death rates * based on population * summary of specific rates * problem when comparing these rates in different populations is that the two populations may have differing distributions of the involved populations * e.g.- Ecuador has a lower crude death rate due to a high birth rate > specific: * Age-Specific Death Rates (ASDRs) * = (# deaths in a particular age group, at a particular place and time/ total population of that age group in that place and time) * based on certain characteristic(s) * logically, death rates are higher with increasing age > standardized: * allows to 'fair' rate comparison * direct/ indirect * YOU NEED TO KNOW HOW TO CALCULATE THESE (see 'rate standardisation') - other specific rates: > crude birth rates > infant mortality rates > neonatal and postneonatal mortality rates > perinatal mortality rate and ratio > maternal mortality rate

Haddon Matrix

- looks at factors related to personal attributes, vector or agent attributes and environmental attributes; before, during and after an injury or death - applies scientific methods to the study of injuries, particularly motor vehicle injuries - 3 phases of injury: 1. pre-injury (improving driver attitudes/ knowledge, anticipating hazards) 2. injury 3. post-injury - factor of injury: > human (alcohol, drugs, inexperience) > vehicle (braking conditions, availability of effective seat belts, tire grip to increase rolling resistance, airbags) > environmental (speed humps, pedestrian crossings guard rails)

risk factors and preventable causes of disease

- major focus for epidemiologists - risk factor: variable which increases a person's chances of developing a disease (e.g.- age, family history, gender, swimming after dark, high fat diet) - preventable cause: avoidable risk factors (e.g.- smoking, unhealthy diet, sexual behavior, reckless driving) - e.g.- despite strong advocacy from health professionals, epidemiologic studies identified that smokers were 11 times more likely to die from lung cancer than non-smokers - findings are drawn from: > population surveys > medical records (from hospitals and clinics) > death registries (death and causes of death are registered) > comparisons with other countries/ regions

preamble to WHO Constitution (1946-48)

- medical model: > focus on disease and illness > complaint, history, physical examination, ancillary tests > diagnosis and prognosis, and perhaps treatment, if needed > but this is ironic, considering WHO saying that health is about all-rounder well-being > "Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity." - is the WHO framework idealistic? ignoring of the dynamic relationship? - e.g.- antidepressants can help with low moods and help people sleep better, but that does not mean that they're happy - successful adaptation: > eustress (good stress) > distress (bad stress) - satisfactory functioning - allostatic load (the wear and tear of the body which increases over time)

vaccine surveillance

- monitoring of vaccine effectiveness, failures and side-effects TYPES OF STUDY DESIGNS FOR VACCINE SURVEILLANCE - randomized field trials: > attack rate = # ill people/ number of persons exposed to the disease > vaccine effectiveness = [(attack rate of unvaccinated people) - (attack rate of vaccinated people) X 100]/ (attack rate of unvaccinated people) > e.g.- Assume in the unvaccinated group that the number of ill persons is 80 out of 100 persons exposed to the disease. Assume in the vaccinated group that the number of ill persons is 1 out of 100 persons exposed to the disease. > e.g.- attack rate of unvaccinated people = 80/ 100 > attack rate of vaccinated people = 1/100 > vaccine effectiveness = ((0.8 - 0.01) X 100)/ 0.8 = 0.79 X 125 = 98.75% of the risk in the disease that can be reduced by the vaccine - retrospective cohort studies (defines individuals on their past vaccine exposure VS present outcomes) - case-control studies (current outcomes VS past exposures; particularly useful when the disease is uncommon) - incidence density: > allows to determine which age the vaccine should be given AND the duration of a vaccine's effectiveness > ID = # new cases/ time of exposure

measures to assess public health

- morbidity: the rate of the presence of disease in a population; level of health and well-being - comorbidity: similar concepts, with multiple diseases ? - mortality: measure of the number of deaths in a particular population - life expectancy: > at birth > at a particular age - years of potential life lost (YPLL) (or years of life lost (YLL)) - years lived with disability (YLD) - disability-adjusted life years (DALYs): > DALYs = YLL + YLD > measure of overall disease burden > expressed as the cumulative number of years lost due to ill-health, disability or early death

social dimension to health prevention

- nutrition - clean, safe environment - individual behaviour - socioeconomic factors: > drive variation in disease and health promotion between and within countries > education (→ employment → increased income) > civil unrest/ war - behaviour: > most important factor when environmental conditions are sufficient > "... process of enabling people to increase control over their health and its determinants, and thereby improve their health" (WHO) > dietary fat intake, consumption of fruit and vegetables, regular physical activity, abstaining from tobacco, adhering to medication regimen, and managing stress and weight

deaths attributable to suicide in Australia

- particularly high in the 25-35 years of age group (20-23% of all age group deaths) - peaks at 30 years of age (23%) - both male-female suicide rates plateau to 0% of age group deaths between 70-85+ years of age ???

secondary prevention: case finding

- periodic health examination: > annual medical examination > age-related check-ups > chronic conditions > e.g.- birth control discussion, prostate check-up, high blood pressure check - health risk assessment: > standardized health questionnaires > risk age (life expectancy/ age in comparison to someone of the same age) - e.g.- thorough medical examination in hospital emergency department

Leavell's model of disease and prevention stages

- pre-disease: > no known risk factors > primary prevention > health promotions/ protection > e.g.- encourage healthy behaviour, "Spit Spreads Death"/ "Spanish Flu Kills, Don't Spit" - disease susceptibility: > primary prevention > specific protection > e.g.- immunisations, supplements, pap smears, prostate exams - latent disease: > secondary prevention > screening > eventually will lead to treatment if a disease is found - symptomatic disease (initial case): > tertiary prevention > disability limitation > e.g.- medical/ surgical prevention - symptomatic disease (subsequent care): > tertiary prevention > rehabilitation > aims to reduce physical and social disability

lung cancer distribution in the United States

- prevalent in the south and southeast - tobacco is grown in 19 U.S. states, mostly in the south and southeast - North Carolina, Kentucky, and Georgia accounts for nearly 80% of total production - also, it turns out that those states in the south and southeast have some of the lowest tax rates on cigarettes in the U.S. - Wyoming and Idaho are outliers

natural history of disease: diabetes

- prevention: > increased physical activity > improvement of nutrition to reduce risk of type II diabetes - treatment: regular injection of insulin in Type 1 diabetes to manage glucose-insulin levels

changing disease patterns and priorities

- priorities of health research and government policies need to change so that changing patterns of disease are appropriately tackled - all countries should review their list of priority diseases for surveillance - there are continuously new threats to population health - e.g.- an incredible spike in primary and secondary syphilis in women (as well as congenital syphilis numbers rising a year later [accounting for gestational period]) in the US (?) during the late 80s

comparing cohort, cross-sectional and case-control designs

- prospective (longitudinal) cohort study: > the past (N/A) > the present (assemble cohorts in the present and collect data on present risk factors → "present exposures") > the future (collects data at a time in the future on outcomes that arise) - retrospective cohort study: > the past (when the exposures occur, it defines the cohorts) > the present (assemble cohorts in the present, based on past risk factors → "past exposures" → and collect data on present outcomes) > the future (N/A) - case-control study: > the past (when the exposures occur, it may be associated with the outcomes) > the present (assemble cases and controls in the present, based on present outcomes, and collect data on past risk factors → "past exposures") > the future (N/A) - cross-sectional study: > the past (N/A) > the present (associations between present exposures and present outcomes → both are occuring at a single point in time) > the future (N/A)

Unwin, et al. (2016) 'Lessons from the history of public health and epidemiology for the twenty-first century'

- public health (1): based on a broad focus on the underlying social and economic causes of health and disease, and their variation in the populations - public health (2): narrow medical focus with the treatment of ill health at its centre - government bodies/ local authorities implement public health measures and think of national standards for food with regards to contamination with unwanted microorganisms or chemical contaminants - early practitioners (Chinese medicine, Hippocrates, Ayurvedic medicine) were aware of season, diet, 'the winds' and lifestyle affecting health - Galen's theory of 'miasma': malodorous and poisonous particles created by decomposing organic matter, which causes disease - contagion theory: disease could be spread by touch, whether of infected cloth or food or people, and recommended quarantine as the best defense - 16th century Italians brought up the concepts of purification within an enclosed space → quarantine → 40 day holds on ships, isolation of victims, washing surfaces with lime and vinegar - John Graunt describes that parish clerks used weekly records of mortality to communicate with members of the public about the extent or absence of an epidemic - epidemiology: study of the distribution and determinants of health-related states or events in human populations and the application of this study to the control of health problems - the core of epidemiology is 'quantitative methods' - James Lind designed the first ever clinical trial (suggesting that scurvy was caused by a lack of fruit intake) - the registration of births and deaths is an essential prerequisite for monitoring disease rates in populations and comparisons between regions - Edwin Chadwick advocated an investment in comprehensive water and sewage systems (deflecting primary causal ideologies of the time → malnutrition, long working hours) - poverty was seen as an effect of disease (as opposed to the cause) - William Farr said, "No variation in the health of the states of Europe is the result of chance. It is the direct result of the physical and political conditions in which nations live." - Farr advocated the need for economic, environmental and social reform (which actually contrasted Chadwick's) - John Snow: > became famous for removing the Broad Street pump, which he saw as responsible for the local residents getting cholera > at the time, there was no knowledge of microbes as agents of infectious disease > the cholera epidemic was raging due to people drinking sewage-contaminated water - the first era of epidemiology was based on engineering, hygiene and sanitation - bacteriological paradigm: laboratory-based diagnosis rather than simply patient complaint - hygiene, overcrowding (and reproductive rates), nutritional status and access to medical care were seen as determinants of infection - investigations linked non-communicable diseases with person-centred health behaviours, however failed to consider broader environmental and social determinants - recently, molecular techniques have been added to the biomarkers of exposure

standardising public health data

- public health data should always be standardised - data standardisation: ensuring that data is internally consistent; each data type has the same content and format - saying proportion of death by suicide of men and women in the 55-59 age group is almost 0% gives off a misunderstanding - the reason this proportion is noticeably small is because there is a very large number of deaths occurring within that age bracket (and suicide falls smaller in proportion to the other causes) - to standardise this data, rates can be used 'per hundred thousand' (as per each age group)

statistical significance

- reading statistics: > 'p' means, the probability that difference of data between the two groups is statistically significant > the larger the difference between the two groups, the smaller 'p' is > the larger the sample size, the smaller the difference will be, the smaller the 'p'-value > even if something is statistically significant, you should ask yourself if it is a substantive difference (e.g.- 170 cm tall VS 170.5 cm tall, p-value= 0.001, but how is this important?) > p < 0.50: * since you want larger differences with smaller 'p'-values, this is not good * here, you would say there is no statistical significance > p = 0.05: * if the experiment was repeated 100 times, there will be a statistical difference 95 times out of 100 * 95 times, the same results (that were found) will show up * NOT 95% confidence behind the difference > p = 0.01 * if the experiment was repeated 100 times, there will be a statistical difference 99 times out of 100 * 99 times, the same results (that were found) will show up * NOT 99% confidence behind the difference > p < 0.01 - risk factor: > characteristics at the biological, psychological, family, community, or cultural level > associated with a higher likelihood of negative outcomes > in order to be statistical significance the risk factor must be present in the persons with the disease more often than persons without the disease - protective factors: > factor is present, occurring significantly less often than in persons without the disease, than in persons with it??????? > decreases the chances of a negative health outcome occurring OR positive-countering effects > e.g.- positive self-image, self-control, social competence - confounding factors: > positive confounding: * when there is a second risk factor involved the presence of the first risk factor becomes significant * the second factor drives the 'action' of the first risk factor * brings about 'spurious' or non-existent association * relation between exposure and outcome will be pulled further away from the null hypothesis > negative confounding: * second variable's significance declines/ disappears * biased/ pushed towards the null hypothesis * e.g.- 'women are better listeners than men' → sex is a negative confounding variable → it's more the differences in personality which drive the listening skills of a person > qualitative confounding: positive association becomes negative

functions of research

- research is fundamental to describing and explaining phenomena - hypothesis testing allows us to examine a prediction of how risk factors/ agents determine health outcomes - good research designs allow us to : > find the disease frequency (between different groups) > determine risk > identify risk factor > minimise bias and confounding

tertiary prevention example 1.2: dyslipidemia

- risk factor for CVD - an abnormal amount of lipids in the blood - increases the chance of clogged arteries (atherosclerosis) and heart attacks, stroke or other circulatory concerns, especially in smokers - often related to obesity, unhealthy diet and lack of exercise - usually causes no symptoms - statin reduces cholesterol (ALL intervention levels) - lipid profiles: > total cholesterol = HDL + LDL + VLDL > [HDL] apoprotein (good cholesterol), which reduces cardiovascular risk > [LDL] (bad cholesterol) which can lead to atherogenesis > [VLDL] triglycerides, which is converted to LDL

tertiary prevention example 1.1: cardiovascular disease

- risk factor modification*: > cigarette smoking > diabetes mellitus > hypertension > sedentary lifestyle > excess weight > dyslipidemia - therapy: > surgical intervention > pharmacological intervention - symptomatic stage prevention: behaviour modification *can help in ALL stages of prevention **not necessarily in this order

indicators of risks

- risk factors (independent variable) - outcome (dependent variable) - usually doesn't consider levels of the disease (e.g.- stage 1 hypertension, stage 2 hypertension) - [a/(a+b)] - [c/(c+d)] (AR)

risks in epidemiology

- risk is an important concept - defined by the proportion of unaffected individuals who experience a risk event (disease, injury, death) during a specified period - when the risk DOES occur, it is called a 'risk event' - risk is not the same for the total population (i.e.- all members of a cohort) - those at risk are the susceptible population - e.g.- pregnancy: > not all women are at risk of pregnancy > only women aged ± 15 - 45 years of age > excluding those on birth control, fertility problems, not engaged with heterosexual behaviour - case fatality ratios: > higher ratios suggest a virulent infection > (# dead/ total population) = (# dead/ # ill) X (# ill/ # infected) X (# infected/ # exposed) X (# exposed/ # susceptible) X (# susceptible/ total population) > e.g.- population = 100, 50% for all other variables * total pop. (100) X susceptibility (50%) = 50 susceptible people * susceptible people (50) X exposure (50%) = 25 people exposed * exposure (25) X infection (50%) = 12.5 people infected * infection () X illness (50%) = 6.35 people ill * illness () X death (50%) = 3.125 people dead * 'case fatality ratio' is the number of people who died and were infected (i.e.- 50% in this case)

effects of sanitation, vector control and environmental impacts

- sanitation: > provision of clean drinking water and adequate sewage disposal > has contributed to reduced infant mortality and increased life expectancy by addressing leading causes of adult mortality > unintended consequence has led to increased population growth and overcrowding - environmental impacts: > overcrowding has lead to increase in environmental degradation > increased pollution > decline in natural habitat (due to urbanisation and forest degradation) > decline in food sources > impact of controlling tsetse fly (sleeping sickness in cattle) > increases in cattle numbers and overgrazing - vector control: > erecting dams in tropical regions led to increase in vectors (e.g.- mosquitoes) > increase in disease (e.g.- malaria, dengue fever)

behavioural counselling

- shared decision-making: > relatively new form of counselling > clinicians and patients reaching neutrally agreed-upon positions on treatments and prevention strategies > clinicians can lead, influence and/ or direct patients to better choices - motivational interviewing: > 3 main issues: 1. patient ambivalence 2. patient opposition 3. lack of cognitive dissonance between current behaviours and goals > 4 main strategies: 1. empathy 2. developing cognitive dissonance 3. recognising resistance 4. supporting self-efficacy - stages (Transtheoretical Model): > precontemplation: * eliciting self-motivational statements * e.g.- "what concerns do you have about your drinking?" * provide objective assessments only * e.g.- "your liver function indicates some damage, likely from your alcohol consumption..." * reflective listening and affirmation * e.g.- "thank you for expressing your concerns... let me pull all this information together..." > contemplation: * increasing cognitive dissonance * e.g.- "there are serious problems around your alcohol consumption, but 'alcoholic' doesn't quite fit" * paradoxical interventions * e.g.- "it's possible that you will never be able to change your actions" * education > action: * providing information on treatment options * continued affirmation * e.g.- "you took a huge step today!" > maintenance: * providing information and support * continued affirmation * e.g.- "what's your biggest reason behind quitting?" > relapse: * increasing self-efficacy * e.g.- "you've been through so much; i admire your commitment so much"

health literacy

- social, cultural and educational factors adversely impact health determination - inability to understand medical information - English is a second language (Government-published documentation is released in multiple languages) - poor health literacy: > more likely to delay using healthcare services > increased hospitalisation and emergency department visits > reduced use of preventative services > reduced ability to interpret health messages - universal health literacy: > redesign patient information for lower ability patients > 'Teach-Back' ("say it back to me") > 'Ask Me 3' (what is my problem? what do i need to do? why is it important for me to do this?)

epidemiology: clinical

- specifically focus on populations within a health care setting - focus is on patients who are at risk or already are afflicted by disease

epidemiologist

- study disease, injury and clinical practice - focus on populations not single cases (patients)

epidemiology: general

- study of factors related to the occurrence and distribution of disease within a population - epidemiological triad of disease causation is the agent, host and environment - Greek roots: > "epi" → upon > "demos" → people/ population > "logos" → study (of)

types of causal relationships

- sufficient cause: > cause always leads to the disease > complete causal mechanism > i.e.- inevitably produces disease > e.g.- having a functioning uterus is a necessary cause of being pregnant → however, you can have a uterus without being pregnant ("sufficient") - necessary cause: > cause is always present when the disease occurs, but the presence of the causes does not always lead to disease > condition that must be present for an event to occur, but will not always produce disease - risk factor: > cause that increases the probability or likelihood of developing the disease > exposure, behaviour, genetics, etc. - e.g.- smoking nicotine is neither a sufficient, nor necessary cause for lung cancer (e.g.- not all smokers will get lung cancer) → rather, it is a risk factor of a variety of diseases

secondary prevention: community screening

- targets disease - target risk factors (biological, behavioural, etc.) - e.g.- screening for breast, colon cancers - argument that they lead to too many false positives and unnecessary invasive procedures - screening process: > perform screening test → negative result → record the result → inform the person screened > perform screening test → positive test → perform diagnostic test → negative result → record the result → inform the person screened > perform screening test → positive test → perform diagnostic test → positive result → start treatment → negative response → revise treatment and reevaluate > perform screening test → positive test → perform diagnostic test → positive result → start treatment → positive response → continue treatment and reevaluate

biological spectrum of disease: iceberg model

- think of a pyramid; bottom to top - exposure without infection → infection without clinical illness → mild illness → severe disease → death - pre-symptomatic VS symptomatic stages of disease - biologic spectrum of disease suggests that there is variation in disease process - not all cases with a disease are identified - iceberg model suggests that the true progression of a disease can only be known when extensive surveillance uncovers the degree of asymptomatic infection - Jekel's study of diphtheria identified: > 2 deaths > 12 with clinical illness > further 32 cases who were asymptomatic, who were identified only by extensive epidemiological surveillance (children in the outbreak area provided throat swabs)

tertiary prevention example 3: diabetes

- type I diabetes: > 5-10% of the diabetic population > lifelong disease > onset is mostly in young adults and children > immune system attacks insulin production - type II diabetes: > 85-90% of the population > acquired disease > strongly attributed to lifestyle and diet - no cure for either - insulin injections and behaviour modification - impact on micro and macrovascular disease (e.g.- retinopathy, kidney disease, heart attack, limb amputation)

vaccinations and immunity

- vaccination: the administration of a vaccine containing a microorganism or virus in a weakened, live or killed state to help the immune system develop protection from a disease - immunity: organisms having adequate biological defenses or tolerance to fight disease - vaccines are effective due to the immunised individual not transmitting the disease - herd immunity: resistance to the spread of a disease within a population that results if a sufficiently high proportion of individuals are immunised to the disease

osteomalacia

- weakening or softening of the bones - most often caused by severe vitamin D deficiency - possible causation: > purdah (religious and social practice of female seclusion) > involves restricting outdoor access or clothes which restrict exposure to sunlight (social/ environmental contexts) > this prevents irradiation of ergosterol which is a molecule which creates vitamin D (biological mechanism) > vitamin D is an essential steroid involved in bone metabolism - case study: sunlight exposure and vitamin D deficiency in Turkish women > women wore traditional or religious clothing which covered large portions of their skin > these areas are usually exposed to sunlight > all patients had vitamin D levels below normal, even in sunny countries > need for vitamin D supplements

role of epidemiologist: newer findings

1. Lyme disease: - causation: infectious agent spread by ticks - treatment: avoid ticks 2. Legionnaires disease: - severe form of pneumonia (lung inflammation usually caused by infection) - causation: infectious agent in untreated air-conditioning systems - treatment: regular treatment of water in air-conditioning systems 3. acquired immunodeficiency syndrome (AIDS): - causation: virus spread primarily through blood - treatment: safe-sex practices and needle sharing programmes

major sources of disease burden in Australia (2011)

1. cancer 2. cardiovascular 3. mental 4. musculoskeletal 5. injuries 6. respiratories

ecology: unintended consequences of resolving health problems

1. childhood infections: - solution: vaccinations - unintended consequences: > decrease in the level of immunity during adulthood > caused by a lack of repeated exposure to infection 2. high infant mortality rate: - solution: improved sanitation - unintended consequences: > increase in the population growth rate > limited available resources > increased pollution 3. malnutrition (and the need for larger areas of tillable land): - solution: erection of large river dams - unintended consequences: > increase in rates of some infectious diseases caused by water system changes that favour the vectors of disease > e.g.- malaria

examples of immunisation

1. diphtheria: - bacterial disease that causes severe inflammation of the nose, throat and trachea - whilst still prevalent, immunized individuals were likely to report mild infections as they were still exposed to the disease - the vaccine's effectiveness declined over time - booster vaccinations provide a temporary, natural booster effect 2. smallpox: - infection of the skin and in the mouth and throat involving raised fluid-filled blisters - vaccine was successfully against both Variola major and minor (both strains of smallpox) 3. poliomyelitis: - eradication of polio again demonstrated the importance of herd immunity - inactivated polio vaccine (IPV) protected immunized individuals - polio virus underwent conformational change (?) and replicated in the intestines - immunized individuals still passed on the virus - sabin oral polio vaccine (OPV) prevented the replication of the virus in the intestine - combination of OPV and IPV was necessary in the Gaza Strip where OPV was ineffective (other intestinal infections prevented the vaccine from being effective)

major sources of disease burden in the United States (2008)

1. heart disease 2. malignant neoplasms 3. chronic lower respiratory disease 4. cerebrovascular disease 5. unintentional injury 6. Alzheimer's disease 7. diabetes mellitus 8. influenza and pneumonia 9. nephritis 10. suicide

four main underlying causes of disease

1. host factors: - characterises of the person which moderates their susceptibility to particular stressors - e.g. genotype; nutrition 2. agents of disease: - causative agent which come in different types - biological agents (allergens, infectious disease, toxins) - chemical agents (chemical toxins) - physical agents (radiation, extreme temperature, kinetic energy (injuries from crashes, bullet wounds, falls)) 3. environmental factors: conditions which promote the likelihood of contact between the host and agent 4. vectors of disease: - organisms which transport and spread an agent of disease - e.g.- West Nile virus is a mosquito-borne disease

epidemiology: four levels to approach the study of disease

1. molecular level (cell biology, genetics, biochemistry) 2. tissue or organ level 3. individual patients (e.g.- sexual activity, alcohol consumption) 4. populations (groups of individuals)

stages of disease

1. pre-disease: period of time before the disease process has begun 2. latent stage: period of time where the disease process has begun, but the disease is asymptomatic 3. symptomatic stage: period of time where the disease is symptomatic

stages of prevention

1. primary prevention: - process of preventing the disease from starting - e.g.- cholesterol in normal range - primary prevention refers to actions aimed at avoiding the manifestation of a disease 2. secondary prevention: - process of preventing progression of disease - e.g.- cholesterol in high range increasing risk of atherosclerosis - secondary prevention seeks to prevent the onset of illness 3. tertiary prevention: - process of slowing or reversing disease progression - e.g.- atherosclerosis is characterized by the deposition of fatty material on their inner walls (which would be evident at this stage) - tertiary prevention aims to reduce the effects of the disease once established in an individual

2X2 tables (assessing risks between different groups)

Attributable Risk (AR) = [a/(a+b)] - [c/(c+d)] ???? a = participants with both the risk factor and the disease b = = participants with the risk factor, but not the disease c = participants with the disease, but not the risk factor d = participants with neither the risk factor nor the disease a + b = all participants with the risk factor c + d = all participants without the risk factor a + c = all participants with the disease b + d = all participants without the disease a + b + c + d = all study participants

rate standardisation

DIRECT - population size (people) X Age-Specific Death Rate (%) = # expected deaths - total # expected deaths = added deaths in all age groups (or whatever sub-group chosen) - crude death rate = total # expected deaths/ total population - this is where standardisation comes in - you may notice that the population of each sub-group for the comparative populations are different (e.g.- 1000 young people in Group A, 4000 young people in Group B) - to standardise the crude death rate, you must add these numbers for each sub-group population (whilst maintaining each group's ASDR) - e.g.- 5000 young people for BOTH Group A and B - the total population should also be added - the crude death rate is calculated the same way (see above) INDIRECT - sometimes, the ASDR is unknown for specific sub-group populations within the wider population - observed death rate = proportion of population X ASDR (%) - if your final total observed death rate is a decimal place, use a constant multiplier - e.g.- an observed death rate of 0.00334 is simply 334 in 100,000 people - you can compare your observed death rate with the death rate reported/ estimated - apply the same ASDRs used in the first population (in which the ASDR was known) - compare the # expected deaths and observed death rate (they are expected to be different) - we need the use the 'standardised mortality ratio' (SMR) - SMR (%) = (# reported deaths/ # expected deaths) X 100 - NOTE: the SMR does NOT use the 'observed death rate' - the SMR (%) suggests the actual death rate being the calculated percentage of the original population - i.e.- a 90% SMR means a 10% reduction of the population expected death rate

uses of risk assessment data example

REMEMBER! 'EXPOSED' MEANS 'WITHOUT THE TREATMENT'... 'UNEXPOSED' MEANS 'UNDERGOING THE TREATMENT'! (see 'numbers needed to treat (NNT) example' for question breakdown help) - experimental group is on some new drug/ intervention, which is being tested in the following study - experimental group, 30% of people had multiple heart attacks, after their first one - control group, 40% of people has multiple heart attacks, after their first one - 100% is the 'total population' - this means: > Absolute Risk Reduction (ARR) = risk (exposed) - risk (unexposed) > ARR = 0.4 - 0.3 = 0.1 ∴ 10% increased risk of heart attacks without the treatment > Relative Risk Reduction (RRR) = (risk (exposed) - risk (unexposed))/ risk (exposed) RRR = (0.4 - 0.3)/ 0.4 RRR = 0.25 ∴ 25% reduction in heart attacks in those who were administered the treatment (in comparison to the control)

numbers needed to treat (NNT) example

REMEMBER! 'EXPOSED' MEANS 'WITHOUT THE TREATMENT'... 'UNEXPOSED' MEANS 'UNDERGOING THE TREATMENT'! - Assume we have an treatment and control group. In the treatment group 15 people report an event whilst 135 report no event. In the control group 100 people report an event whilst 150 report no event. Calculate the ARR, RRR and NNT. - acknowledge that you have both an experimental group and control group - in both, there will be events and non-events (and totals for each) - events (experimental) = 15 - non-events (experimental) = 135 - events (control) = 100 - non-events (control) = 150 - you need to calculate the proportion WITH events (using the total in each experimental/ control group) - proportion (events) = events/ total events + non-events in one group ??? > proportion with events (exp.) = 15/ 150 = 0.1 > proportion with events (con.) = 100/ 250 = 0.4 - ARR = proportion (events → exposed) - proportion (events → unexposed) = 0.4 - 0.1 = 0.3 - RRR = ARR/ risk (exposed) = 0.3/ 0.4 = 0.75 (75%) - NNT = 1/ ARR = 1/ 0.3 = 3.33 ∴ 3 patients treated result in benefit to 1 patient

numbers needed to harm (NNH) example

REMEMBER! 'EXPOSED' MEANS 'WITHOUT THE TREATMENT'... 'UNEXPOSED' MEANS 'UNDERGOING THE TREATMENT'! - Assume we have an treatment and control group. In the treatment group 75 people report an event whilst 75 report no event. In the control group 100 people report an event whilst 150 report no event. Calculate the ARR, RRR and NNH. - acknowledge that you have both an experimental group and control group - in both, there will be events and non-events (and totals for each) - events (experimental) = 75 - non-events (experimental) = 75 - events (control) = 100 - non-events (control) = 150 - you need to calculate the proportion WITH events (using the total in each experimental/ control group) - proportion (events) = events/ total events + non-events in one group > proportion with events (exp.) = 75/ 150 = 0.5 > proportion with events (con.) = 100/ 250 = 0.4 - ARI = proportion (events → exposed) - proportion (events → unexposed) = 0.4 - 0.5 = -0.1 however, ARI must always be positive = 0.1 - RRR = ARI/ risk (exposed) = 0.1/ 0.4 = 0.25 (25%) - NNH = 1/ ARI = 1/ 0.1 = 10 ∴ 10 patients treated result in harm to 1 patient

procedures of epidemic investigations

STANDARD - establish diagnosis: > incorrect diagnosis can lead to the provision of inappropriate treatments > can cause unnecessary public anxiety - define case definition: > form a list of symptoms criteria, which allows health workers to preliminarily identify whether or not an individual has a disease (before clinical diagnosis confirmation) > not 100% correct: * false positive (individuals wrongly defined as having a disease) * false negatives (individuals wrongly defined as NOT having a disease) - establish epidemic occurrence: > is it endemic or epidemic? > endemic: the number of cases are in line with the information known about the nature and pattern of the disease > epidemic: the data depicts a departure from the typical nature and pattern of the disease - characterize epidemic: > TIME: * was the exposure a single event, or did it involve multiple exposures between individuals? * likely route of infection based on time * approximate time of exposure * helps distinguish between primary and secondary cases * data may show no exact 'peak time' of the original infection (particularly if the infection has been going on for an elongated period of time) > PLACE: * understanding the geography behind the epidemic * can identify source and/ or secondary places of transmission * 'spot maps' do not indicate the population density * e.g.- John Snow was able to pinpoint the source of the cholera outbreak in a small pub in London * e.g.- the Black Spot Program identifies dangerous road locations to improve road safety * other map types can map out the geographical trail of a disease across an expanse of space * e.g.- trail maps of the spread of cholera in South America > PERSON: * index case: the first identified case in a group of related cases of a particular communicable or heritable disease * age groups * e.g.- high rates of measles in children under 1 year of age suggests poor vaccination rates HOWEVER high rates of measles in children ages 10+ suggests good vaccination adherence in infancy, but a declining effect of this original vaccine INVESTIGATIVE REPORT - hypotheses (of): > source (e.g.- drinking from the same water source) > spread (e.g.- person-to-person spread) > transmission (e.g.- door handles, taps, escalator railings) - confirmation of hypotheses: > laboratory studies > case control (involves comparing affirmative-case studies and negative cases, and potential differences in exposure factors) - control measures: > sanitation (involves removing the agent from the source of infection, removing index case from population and removing susceptible people from agent exposure) > prophylaxis (providing a barrier to infection, e.g.- vaccination, condoms) > diagnosis/ treatment (prevents the spread of infection from primary to secondary sources) > control of vectors (e.g.- DDT was used as a way to control mosquito numbers and reduce malaria rates) - follow-up surveillance: > confirms whether or not long-term interventions where successful in controlling the infection > active VS passive

types of (epidemiological) research designs

SUMMARY! - observational studies (see below): > descriptive studies > ecological studies > cross-sectional studies > case-control studies > cohort studies - experimental studies: randomised controlled trials (RCTs) → clinical VS field/ community **the following have been listed in increasing strength of epidemiological evidence (exceptions: longitudinal ecological studies are stronger than cross-sectional ones & prospective cohort studies are stronger than retrospective ones) ***longitudinal studies are preferred to cross-sectional ones ****controlled trials are deemed to be an experimental requirement DETAIL! - observational studies can be used to: > identify the distribution of disease and develop hypotheses > test the proposed hypotheses - qualitative study design: > have derives from social and ethnographic research, which does not have any predefined notions of the topic being studied > often use open-ended interviews with individuals or with focus groups (e.g.- food contamination, modes of disease transmission) > they can pick up on themes and patterns and attribute them to the nature history of the disease > quantitative techniques used to test hypothesis/ research design > biased towards/ against the sample interviews or poorly representative - quantitative study design: > describes phenomena using statistics and numerical data > includes ecological designs (disease frequency and related risk factors) and survey designs (individual health response and status) > strength of survey designs is that they allow us to precisely examine the association between disease and various risk factors at the individual level > in ecological designs, we only know about the prevalence and risk factors within the population (i.e.- we cannot ascertain whether or not the same individual who has the disease also has the risk factor, unlike survey designs) > this means, people mistakenly make an inference from an ecological design, saying an association exists ("ecological fallacy") - cross-sectional survey design: > method for hypothesis generation > examining individual data this way does not allow us to determine a temporal association between risk factor and disease > usually provides information on prevalence rather than incidence > more useful for descriptive epidemiology than it is for analytic epidemiology > synonymous with survey - case-control studies/ cohort studies: > may reflect individuals who share a common risk factor OR are representative of the population > longitudinal studies have repeated interviews over an elongated period of time, whereas cohort studies have it usually one time > case-control groups are nested within the cohort study > prospective cohort design: * cohort is measured at baseline * data and existing risk factors are collected * cohort is followed up for a set period of time, where various (disease) outcomes can be collected * this means that multiple interviews and interactions can occur with the participants * OR, the baseline data can be compared with data from other trusted data sources (e.g.- medical records, Medicare, medication prescriptions, hospital admissions, death registries) > retrospective cohort design: * regards current health outcomes, but also past risk factors and exposures * e.g.- what age did you start smoking? * impacted by bias (e.g.- recall bias)

natural history of disease (2)

THERE IS ANOTHER CARD ON THIS! - stages of disease: > pre-disease stage (asymptomatic) > latent stage (disease process has begun) > symptomatic stage (symptoms are present) OR - exposure (to toxins, smoking, etc.) → stage of susceptibility - pathological changes → stages of subclinical disease - onset of symptoms - stage of clinical disease → usual time of diagnosis - stage of recovery, disability or death - stages of prevention: > primary prevention (prevents the disease process from beginning) > secondary prevention (interrupt the disease process when it has begun) > tertiary prevention (limits the impact of the disease)

CDC's question banks

https://www.cdc.gov/csels/dsepd/ss1978/index.html

(a)etiology

the science/ study of the causes or origin and the factors which produce or predispose toward a certain disease or disorder