QUM all notes - Hi these are the lectures i made for QUM, they were really helpful. PDF

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Hi these are the lectures i made for QUM, they were really helpful....

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Lecture 1 – The aims and objectives of the National Medicines Policy Aim: To meet medication and related service needs so that both optimal health outcomes and economic objectives are achieved. Objective:  Timely access to medicine @ affordable cost that community can afford  Quality use of medicine  Maintaining responsible and viable medicine industries  Medicines need to meet appropriate standards of quality, safety and efficacy – How quality use of medicines link with the National Medicines Policy

– How quality use of medicine is defined QUM is defined as the appropriate supply of medicine to a consumer in that it meets their own individual requirements for an adequate period at the lowest cost to them and community. – The stakeholders in achieving quality use of medicines, and what role these stakeholders have in achieving quality use of medicines  RESPONSIBILITY OF ALL STAKEHOLDERs - Recognizing problems, identifying factors that contribute to those problems, initiating interventions to improve medication use and evaluating outcomes - Improve risk/benefit understanding of medicine use - Encouraging informed debates about the role of QUM - Working collaboratively to achieve QUM 

The Australian government - Developing and implementing the National Strategy for QUM - Coordinating relevant government programs - Investigating and developing appropriate structures, funding mechanisms, legislation and environments that support QUMs

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Healthcare professionals - Assisting consumers to make educated decisions - Increase benefits/risk of medicines - Developing skills to promote appropriate use of medicine - Increasing awareness of the place of medicines in society - Use objective information, resources and services to make correct decisions on medicine use

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Consumer - Asking for information and utilizing information - Increasing awareness of risk/benefits of medicines - Developing skills to use medicine appropriately - Increasing awareness of the place of medicines in the broader context of health services and society

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Media - Reporting accurately on medicines and health care issues - Encouraging messages that enhance QUM

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Health and aged-care facilities

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Providing facilities, systems, training opportunities and structures that support staff, health practitioners and consumers in using medicines wisely and avoid medication errors

Health care funders and purchasers - Funding or purchasing services to support QUM - Providing appropriate funding mechanisms that give consumers and health practitioners rewards to support QUM

– The national strategy to achieve quality use of medicines 5 national strategy: - Improve QUM by health care consumers - Improve QUM by health practitioners, providers and educators - Gain the commitment of the medicines industry, including manufacturers and distributors, to QUM - Gain the commitment of governments to QUM - Improve the commitment of all stakeholders to working in partnership to achieve QUM.

– The types of activities that are currently performed (and could be performed) that aim to improve the quality use of medicines.  National and state programs to promote qum - National Prescribing Service (NPS): NPS MedicineWise o Educational visits to GPs o National Prescribing Curriculum o Provision of objective information o publications and telephone hotlines for consumers - Australian national formulary o AMH - Therapeutic Guidelines o Objective, evidence-based guidelines for multiple common disease states - State Government (SA Health) QUM Activities o APAC Guidelines for Pharmaceutical Reform in public hospitals o Medication management and management of acute pain 

Community/hospital programs to promote qum - HMR and RMMR - MedsCheck and Diabetes MedsCheck - Hospital Protocols/Guidelines and Management Pathways

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Pharmacotherapeutics/ pharmacy practice to achieve qum - Selecting the right drug for the right patient at the right time in the right dose for the right duration of time - Implementation of appropriate follow-up plans following treatment initiation - Good counselling of patients (consumers) such that they are able to take the medicine as instructed (i.e. facilitate good adherence to treatment) - Counselling will include advice on how to monitor for efficacy and toxicity, and what to do if/when either of these occur Research to achieve qum

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Lecture 2 

Demonstrate a general understanding of the definition of epidemiology

Epidemiology is primarily concerned with the study of disease and how it detracts from human health. It focuses on groups / populations of individuals, not the individuals themselves.  Describe / predict why certain groups of people develop diseases whereas others do not. Pharmacoepidemiology: Narrowing epidemiology down to the study of medicines (pharmaceuticals)  Typically applied to investigation of adverse drug reactions  There are two types of adrs type A and type B  Type B are much more unpredictable and non-dose related and hence are typical focus of Pharmacoepidemiology

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Understand, be able to represent and perform calculations regarding:

Ratio and proportion:  Proportion = Number of a population who have disease X  This can be expressed as % (proportion) or a number between 0 to 1 (ratio) EXAMPLE: Quality Use of Medicines has 145 pharmacy students enrolled, and 107 of these are females. On Friday 6th March, 92 were present at the lecture n=145 -the proportion (percentage) of the population that are female = 107/145 (74%) ie ratio is female= 0.74 -the proportion (percentage) of the population that is present at the lecture = 92/145 (63%) ie ratio = 0.63

Rate and Frequency:  Rate and frequency are a time specific ratio or proportion  has units of time, whereas proportion and ratio do not. EXAMPLE Quality Use of Medicines has 145 pharmacy students enrolled, and during the study period (total duration 5 months), 25 attended a General Practitioner. What frequency (and ratio) saw a GP? n=145 ->Average number who saw a GP each month = 25 / 5 months = 5 per month -> The rate (or frequency) that pharmacy students saw a GP is 5/145 = 3.4% per month

Prevalence:  The amount something (e.g. a disease) is present in the population  Most useful in chronic diseases  Expressed as a ratio or percentage and has no units 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑓𝑓𝑒𝑐𝑡𝑒𝑑 𝑖𝑛𝑑𝑖𝑣𝑢𝑙𝑎𝑠 𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 𝑖𝑛 𝑡ℎ𝑎𝑡 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 EXAMPLE: In South Australia there are 220 000 people aged 65 years and older. Of these people, 6730 have heart failure. So, what is the prevalence of heart failure in South Australians aged 65 years and older? 6730 𝑝𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 = → 0.0306 (3.06%) 220000 Incidence (incidence proportion):  The rate or frequency of occurrence of something (e.g. a disease) in the population

 Incidence is usually expressed as the incidence proportion (the rate of developing the disease per unit time) but is usually referred to as the ‘incidence’. EXAMPLE: In South Australia there are 220 000 people aged 65 years and older. Of these people, 6730 have heart failure. Over a 5year follow-up, there were 2552 new cases of heart failure diagnosed. So, what is the incidence proportion of heart failure in South Australians who are aged 65-years and older? 𝑟𝑖𝑠𝑘( 𝑜𝑓 𝑑𝑒𝑣𝑒𝑙𝑜𝑝𝑖𝑛𝑔 𝑡ℎ𝑒 𝑑𝑖𝑠𝑒𝑎𝑠𝑒) 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 = 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑓𝑜𝑙𝑙𝑜𝑤 − 𝑢𝑝 𝑁𝑒𝑤 𝑐𝑎𝑠𝑒𝑠 2552 𝑃𝑜𝑢𝑙𝑎𝑡𝑖𝑜𝑛 @ 𝑟𝑖𝑠𝑘 𝑟𝑖𝑠𝑘 = 22000 − 6730 = 213 270 → 1.2% → 0.24% / 𝑦𝑒𝑎𝑟 → 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 = 5 𝑦𝑒𝑎𝑟𝑠 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑓𝑜𝑙𝑙𝑜𝑤 − 𝑢𝑝 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑓𝑜𝑙𝑙𝑜𝑤 − 𝑢𝑝 𝑟𝑖𝑠𝑘 =

Risk:  The likelihood that something specific will occur to an individual EXAMPLE: the proportion of unaffected individuals who will contract a disease of interest over a specified period (the observation period). 𝑁𝑒𝑤 𝑐𝑎𝑠𝑒𝑠 𝑟𝑖𝑠𝑘 = 𝑃𝑜𝑢𝑙𝑎𝑡𝑖𝑜𝑛 @ 𝑟𝑖𝑠𝑘  range between 0 and 1 OR as a percentage  risk has no units per se, but it is often expressed as risk per unit of follow-up time In 2018 there were 5031 episodes of patients with cancer being admitted to the Royal Adelaide Hospital, and of these 596 were associated with an infection. 𝑁𝑒𝑤 𝑐𝑎𝑠𝑒𝑠 𝑟𝑖𝑠𝑘 = 𝑃𝑜𝑢𝑙𝑎𝑡𝑖𝑜𝑛 @ 𝑟𝑖𝑠𝑘 596 𝑟𝑖𝑠𝑘 = → 0.118 𝑂𝑅 11.8% (𝑝𝑒𝑟 ℎ𝑜𝑠𝑝𝑖𝑡𝑎𝑙 𝑎𝑑𝑚𝑖𝑠𝑠𝑖𝑜𝑛) 5031

Population at risk:  The population of interest that can develop the disease  People who already have it are not considered at risk and hence excluded from the population EXMAPLE: Of 5031 patients who had cancer and were admitted to the Royal Adelaide Hospital in 2018, 342 had neutropenia at the time they were admitted to the hospital and 254 developed neutropenia at some time during their hospital admission. Question, What is the risk of developing neutropenia during hospital admission? Population at Risk = Total Population – those already with the condition of interest Population at Risk = 5031-342 = 4689 𝑁𝑒𝑤 𝑐𝑎𝑠𝑒𝑠 𝑟𝑖𝑠𝑘 = 𝑃𝑜𝑢𝑙𝑎𝑡𝑖𝑜𝑛 @ 𝑟𝑖𝑠𝑘 254 𝑟𝑖𝑠𝑘 = → 0.054 (5.4%) 4689

Diagnostic testing:  Not perfect, sometimes they might show a positive for someone that doesn’t have the disease and other times they might have it but might show negative.  For the reason above diagnostic testing is needed to be well justified as it’s a cost to the health care system, therefore try to use them to confirm a diagnosis, not as a fishing expedition. - Does the truth (they have the disease) and the test result agree: TRUE OR FALSE -What is the result of the test: POSITIVE OR NEGATIVE TRUE, positive: The results and the truth agree, positive ie has it -> hence this person was screened and was found to have the disease and he does have it TRUE, negative: The results and the truth agree, negative ie doesn’t have it -> hence this person was screened and was found to not have the disease and he doesn’t False, postive: The results and the truth don’t agree, positive ie has it -> hence this person was screened and was found to not have the disease BUT the truth is that he does have it False, negative: The results and the truth don’t agree, negative ie doesn’t have it -> hence this person was screened and was found to have the disease BUT the truth is that he doesn’t Specificity:  the probability of a negative test in a non-diseased person 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 + 𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒  useful for understanding the utility of the test on the population as a whole  generally independent of disease prevalence

Sensitivity:  the probability of a positive test in a diseased person. 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 + 𝐹𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒  useful for interpreting test results of an individual  depend on the underlying risk of the population being tested  derived from sensitivity, specificity and prevalence values and are specific to a population Positive predictive values:  Someone has a positive test result – what is the probability that they have the disease in question 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑃𝑃𝑉 = 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 + 𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 Negative predictive values:  Someone has a negative test result – what is the probability that they do not have the disease in question

𝑁𝑃𝑉 =

𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 + 𝐹𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒

False negative:  Important to minimise it, when an undiagnosed disease will have substantial negative health outcome that could have been avoided via treatment. If having the disease and not being diagnosed does not result in major harm, then false negatives aren’t important.  Testing with increasing sensitivity minimise false negatives False positive:  Important to minimise it - When treatment or follow-up procedures have high risk of harm and/or are highly expensive - decision on treatment are based on the test/no confirmatory tests available  its not important if the follow up procedure are low risk and inexpensive or if further diagnostic test have lower false positive rates  Testing with increasing specificity minimise false positives

Lecture 3 

Understand different methods for reporting health related events, including incidence proportion, incidence curves and survival curves.  A health-related event is any important change or outcome, eg: - death, recovery, progression of a disease, significant change in a clinical sign  events can be expressed as: - Proportion (most common) - Curves - Odds - Hazard - Time (Average time until an event occurs)  incidence proportion: - a measure of the risk (likelihood) of new cases of a specific health related event in a specific population at risk over a specific time, example: Risk of death over the next 10 years = 20% - however, if the risk changes over time then the next 10 years risk of death will be different, therefore this maybe misleading  incidence curves - express incidence across a defined time frame, ie from ages 0 to 100 whereas incidence proportion specific time frame ie every 10 years - Not limited to a specific time frame - Can detect a change in risk over time  Survival curves: - the proportion of individuals that are alive after a specific period - this complements incidence curves and describes how survival changes over time - EXAMPLE: If survival is 70% over 10 years therefore, 30% have died in this period ie the risk of death over the next 10 years = 30%

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Understand the limitations with using word descriptors of risk.  A person’s word description of risk can be interrupted very differently to others for example ‘likely' and ‘possible’ might mean different things to different people.

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Understand, be able to represent and perform calculations regarding:

 Framing of risk, example: A treatment that “saves eight lives out of 10” seems better than one that “fails to save two in every 10” - Losses can seem larger than gain  Probability vs odds - Odds: express the frequency of an event over a specific time frame, commonly used for betting      𝑅𝑖𝑠𝑘 (𝑝𝑟𝑜𝑝𝑎𝑏𝑖𝑙𝑖𝑡𝑦) = vs 𝑂𝑑𝑑𝑠 =   

-

  

Odds can be larger than 1, probability ranges from 0 to 1 If event(disease) is rare then odds ≈ probability If event(disease) is not rare then odds >> probability

 The hazard of events occurring. - Hazard is the instantaneous risk at a specific moment in time whereas incidence proportion is risk over a time period - Drives the change in survival over time, it’s the gradient of survival/incidence curve over time

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Describe how various hazard rates relate to survival curve Bathtub- Appropriate for most human populations, where there is an initial period of increased mortality, followed by a hazard rate of almost zero, which eventually starts to increase again later in life. Decreasing- Typically following the diagnosis of most types of cancer, when mortality is high immediately following diagnosis, and then decreases over time as patients are cured. Constant- Appropriate following the diagnosis of some types of cancer (e.g. prostate and breast cancer), where the excess mortality is relatively constant over time and persists for 15-20 years after diagnosis

Lecture 4 – Differentiate between different types of risk factors  Modifiable - When a risk factor can change within a subject, either spontaneously or as a result of an intervention - Egs. Blood pressure & body mass index  Non-modifiable - These are risk factors that cannot change - Gender, ethnicity, date of birth/age  Causal

– Understand when modifying a risk factor is a potential means of modifying risk  Association can predict event risk ie homocysteine levels predict cardiovascular risk (higher levels associated with higher risk)  Causation has the ability predict/ prevent events by modifying the factor whereas association can’t do this. – Calculate, interconvert and interpret basic measures of risk modification including EXAMPLE: Group A (wear seatbelt when driving a car) => Mortality 15% Group B (Did not wear a seatbelt when driving a car) => Mortality 20%  Relative risk - When we compare outcomes across 2 groups, what is the ratio of the risk between one group compared to the other -

𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑅𝑖𝑠𝑘 =

 ()  ()

=>

% %

=> 0.75 ie. use of a seatbelt reduces the risk to 75% of

what it would have been

 Odds ratio - 𝑂𝑑𝑑𝑠 =

 ()  ()

 Relative risk reduction - only used in cases where the exposure decreases risk (wearing seat belt -> lower risk) (efficacy endpoint of drug treatment) - RRR = 1 – relative risk - RR = 75% → RRR = 1 - 0.75 = 0.25 = 25% - Use of a seatbelt reduces the risk by 25% of what it would have been without use of a seatbelt .    => 25% => - Second way to calc 𝑅𝑅𝑅 =  ()

.

- make difference appear larger, especially when baseline risk is small ie used in promotions of drug  Absolute risk reduction - When we compare outcomes across 2 groups, what is the absolute difference in risk between the 2 groups. - Change in absolute risk = Risk in Group A – Risk in Group B => Risk with seatbelts– Risk without seatbelts - Change in absolute risk = 15%– 20% => -5% - WORD: The risk associated with seat-belt use is 5% lower than the risk would be if you did not use a seatbelt - Dependent on baseline risk

 ARR vs RRS in two different scenarios Baseline risk/ treatment risk 40% and 30% 10% and 7.5% RRR 10% / 40% => 25% 2.5% / 10% => 25% ARR 30% - 40% => 10% 7.5% - 10% => 2.5%  Number needed to treat - More user-friendly reporting of absolute risk - the number of patients that would need to be treated for one event to be avoided over a specified time period  - 𝑁𝑁𝑇 = ie, NNT and ARR are inversely proportional. If ARR increases, the drug is    more effective therefore a smaller number of individual need to be treated  Number needed to harm - number of individuals that need to use the drug for a specific time period to cause one additional adverse event - Want small NNT and large NNH  - 𝑁𝑁𝐻 =   

– Differentiate between the uses for, and interpretation of, relative and absolute risk differences – Relationship between hazard ratio and other risk modification measures  ()  𝐻𝑎𝑧𝑎𝑟𝑑 𝑅𝑎𝑡𝑖𝑜 =  ()

 Derived from comparing 2 survival curves

Lecture 5 – Explain the difference between a population and a sample  Population: Can be very large, however it’s usually impossible to include the whole population in a study due to cost and ethical factors.  Sample: Therefore, we take samples at random of population and use this as an average for the population – Explain and interpret random error  Sampling error: If you pick one sample size from a population is very unlikely the next sample you pick would have same results and hence this introduces random error.  This is due to heterogeneity  Measurement error: eg. time of measuring the individual weight….ie at morning people tend to weigh less.  IF a study comparing two medicines had an RR = 1 this would mean that the two drugs had similar outcomes in terms of efficacy. However due to random error it very unlikely even if you compare two of the same drugs that RR would equal 1 because of how effective the drug is in two different samples. – Identify and explain factors influencing  statistical significance When is it easy to show statistical significance?  The treatment has a large effect (e.g. RR of 0.5 vs 0.85)  The study has a large sample size (narrower confidence intervals and greater certainty)  The study population is more homogeneous



p values (probability value)

Null hypothesis (Ho): There is no difference in efficacy between drug A and drug B Alternate hypothesis (HA): is that there is a difference in effect between drug A and drug B P value: probability of obtaining test results at least as big as the results observed during the test, assuming that the null hypothesis is correct. IF P = 1.0, its 100% lik...