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FPH exam > 3B sickness and health > Flashcards

3B sickness and health Flashcards

1
Q

Routinely recorded mortality data: STATS19 (information, collection, uses, strengths and weaknesses)

A

INFORMATION
- injury or deaths from road traffic collisions

COLLECTION
- any RTC involving personal injury is recorded at the time by the police on a STATS19 form
- forms are collated by police and data is sent to department for transport who adds info to National Road Casualty Database

USES
- gives an indication of mortality from RTCs
- can be liked to data from A+E on injuries secondary to RTCs

STRENGTHS
- contains more information on type of vehicles etc than A+E reports
- may include incidents that did not present to ED

WEAKNESSES
- police do not attend all RTCs
- may be differences between how police and health services rate morbidity

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2
Q
A
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3
Q

Routinely recorded mortality data; give 2 sources of UK routinely recorded mortality data

A
  1. death certificates/ death registrations
  2. STATS19 (data on mortality secondary to RTC)
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4
Q

Routinely recorded mortality data: describe data from death certificates/ death registration and its strengths and weaknesses

A
  • collated by ONS

INFORMATION
- cause of death and contributory factors
- place of death and time/date of death
- demographics (name, dob, address)
- place of birth
-occupation
-spouse/civil partner

COLLECTION,CODING AND ANALYSIS
- deaths have to be registered within 5 days
- deaths must be registered before a funeral can occur
- cause of death is coded by ONS using ICD-10 codes
- data is published annually by ONS

USES
- calculating life expectancies
- comparing regions and trends over time
- health needs assessment for serious conditions

STRENGTHS
- complete
- timely
- relatively accurate
- coding accuracy tends to be good as coded centrally by ONS

WEAKNESSES
- accuracy of cause of death less good for older people with multiple co-morbidities
- data may not be accurate ie occupation due to occupational advancement (tendency to increase socioeconomic status of people who have died)
- may be problems comparing over time when switched from ICD-9 to ICD-10

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5
Q

Routine sources of morbidity data

A

PRIMARY CARE
- CQRS system
- clinical practice research datalink

SECONDARY CARE
- hospital episode statistics

SURVEYS
- Integrated household survey
- Health Survey for England

CONDITION SPECIFIC REGISTRIES AND DATASETS

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6
Q

Routinely recorded morbidity data: describe data from the CQRS system (description, collection, coding and analysis, uses, strengths and weaknesses)

A

DESCRIPTION
- system used in GP primarily to calculate GP payments
- collects data on GP practices and calculates payments based on
1. Quality and outcome framework indicators
2. nationally commissioned services
3. locally commissioned services

COLLECTION, CODIING AND ANALYSIS
- extracts from GP may be sent to CQRS via the GP extraction service or other electronic means
- payments are linked to evidence based indicators through Quality and outcomes framework
- CQRS is accessible to staff working in public health departments

USES
- payments to GPs based on services delivered and degree to which QoF indicators are met
- registers provide an estimate of disease prevalence
- useful when planning new services

STRENGTHS
- as system is used for calculating payment there is an incentive for GPs to ensure data accuracy and completeness
- relatively complete since most people are registered with GP
- many conditions are treated nearly exclusively in primary care
- QoF score provides an indication of the quality of care provided by the practice

WEAKNESSES
- primarily designed for collating information for payments not for collecting information on disease prevalence etc
- accuracy depends on coding by the GP
- cannot use QoF to compare practices as affected by list size and population characteristics
- QoF is voluntary although most practices participate.

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7
Q

What are quality outcome framework indicators?

A

The quality outcome framework is a voluntary annual incentive and reward scheme for all GPs.

Practices score points for their level of achievement against certain indicators.

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8
Q

Routinely recorded morbidity data: describe data from the Clinical Practice Research Datalink (description, collection, coding and analysis, uses, strengths and weaknesses)

A

INFORMATION
clinical practice research datalink collates data from primary and secondary care, including the following
1. Data from GP electronic records
2. HES
3. primary and secondary care prescribing data
4. Mortality data
5. Disease registers

COLLECTION, CODING AND ANALYSIS
- data is coded differently in each of these datasets but it can be linked via NHS number

USES
- pseudonymous data is provided for research purposes

STRENGTHS
- linked data from multiple sources for a large number of patients
- longitudinal data primary care data available
- primary care data also includes lifestyle information (ie smoking)

WEAKNESSES
- data from many of these sources is incomplete
- not all GP practices participate
- to access data requires payment

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9
Q

Routinely recorded morbidity data: describe data from HES (description, collection, coding and analysis, uses, strengths and weaknesses)

A

INFORMATION
-Information is recorded from all hospital admissions including:
1. age, DOB, postcode, ethnicity
2. start and end date of admission, ward
3. Diagnosis, investigations, and procedures
- a limited amount of information is also recorded for outpatient appointments and A+E attendances

COLLECTION, CODING AND ANALYSIS
- hospital episodes are coded locally by clinical coders (not clinicians) who use ICD10 codes and OPCS4 (classification for interventions and procedures)
- data collected monthly as part of mandatory submission by hospitals
- data is then
1. sent to Secondary Uses Services who provide data to trusted organisations ie for commissioning
2. Cleaned and published in HES dataset

USES
- Used for commissioners to pay providers
- used to analyse hospital usage and waiting time
- assess quality of care and outcomes
- estimate health needs for conditions primarily managed in secondary care

STRENGTHS
- relatively complete (hospitals must submit in order to be paid)
- timely (submitted monthly)
- Can be linked to mortality data to generate stats that link episode to outcome at the individual level

WEAKNESSESS
-accuracy depends on medical notes completeness and the clinical coder
- Variable completeness
- relates to episodes not patients (may overestimate need if one patient has multiple admissions)
- only useful for conditions that are generally admitted to hospital
- only NHS data (not private hospitals)

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10
Q

Routinely recorded morbidity data: Integrated household survey

A

Integrated household survey combines:
- General Lifestyle Survey
- English Housing Survey
- Living Costs and Food Survey
and others!

  • conducted annually by ONS
  • 200 000 households
  • generates data on:
  1. economic acitivity
  2. education
  3. alcohol consumption
  4. smoking
  5. illnesses
  6. consultations with healthcare professionals
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11
Q

Routinely recorded morbidity data: health survey for England

A
  • Annual survey
  • conducted by national centre for social research and UCL
  • covers around 6000 households
  • involves an:
    1. interview (smoking, alcohol and other questions)
    2. examination by nurse (height, weight, BP etc)
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12
Q

international health data sources: WHO Databases

A
  • WHO regional departments maintain databases of health statistics
  • ie European Health for All database
  • collates statistics on demographics, health, risk factors and health services for countries in the WHO region
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13
Q

international health data sources: Global Burden of Disease Study

A
  • commissioned by World Bank to compare burden of disease and risk factors across the world
  • first in 1991
  • now provides annual estimates of burden of disease for 371 disease and injuries across 204 countries from 1990 to present
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14
Q

international health data sources: Demographic and health surveys

A
  • funded by USAID
  • nationally representative household surveys in developing countries
  • provide data on fertility, health etc
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15
Q

Bias

A

Systematic error that leads to a difference in how the comparison groups are selected, treated, measured or interpreted

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16
Q

Sources of bias in population data

A
  • Response bias (selection bias)
  • GP list size
  • Occupational advancement
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17
Q

Sources of bias in population data: response bias

A
  • type of selection bias
  • particular problem with routine data collecting surveys
  • even in the census certain groups are less likely to respond and therefore will be under-represented
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17
Q

Sources of bias in population data: GP list size

A
  • GPs often delay or never remove patients who have died or moved away
  • leads to a systematic error regarding estimations of population size and structure
  • this can lead to an underestimation of service provision (ie vaccine uptake)
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18
Q

Sources of bias in population data: Occupational advancement (status inflation)

A
  • there is a tendency for people to increase the socioeconomic status of people who are deceased when filling out surveys or death certificate information
  • this is known as occupational advancement and can cause bias in social class data
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19
Q

Population data: what is artefact

A
  • Spurious differences between and observed population characteristic and the actual population characteristic
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20
Q

Population data: give 3 examples of when artefact can arise

A

CHANGES IN CLASSIFICATION
ie when ONS changed from coding mortality data using ICD-9 to ICD-10. This made comparing mortality data from before and after the change challenging

CHANGES IN QUESTIONS OR POSSIBLE RESPONSES
ie when the census included ‘mixed’ as an ethnic option

CHANGES IN GEOGRAPHICAL SUBDIVISIONS ie when the census moved from electoral ward to output areas in 2001

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21
Q

Methods of classification of disease: the international classification of disease - what is it

A
  • one of the WHO family of classifications
  • Provides a common language for reporting, monitoring and recording diseases
  • primarily an international standard classification for mortality statistics
  • revised every 10-20 years
  • it is problematic to translate codes between revisions so in the cross over period dual coding should be used
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22
Q

Methods of classification of disease: What is the ICD used for?

A
  • enables consistent collection, analysis and presentation of data allowing comparisons to be drawn over time and between geographies

ICD is particularly used in:
- analysis of population health
- monitoring disease frequency
- classification of death certificates and hospital records
- mortality and morbidity stats to be collated using a common framework

23
Q

Methods of classification of disease: ICD-10

A
  • despite ICD-11 being available the ONS still uses ICD-10 for death certificate classification
  • uses alphanumeric codes so potential for confusion between ‘zero’ and letter ‘o’
  • some codes are reserved for emerging diseases
24
Q

Methods of classification of medical care: healthcare resource groups

A
  • HRGs are standard groupings of clinically similar treatments which require comparable levels of healthcare resource
  • HRGs are often groups of ICD-10 diagnoses or OPCS procedures that have similar resource implications
  • HRGs are used as means of determining fair and equitable reimbursement for care services delivered by providers
  • HRGs are the unit of currency for commissioning and paying for health services
25
Q

Routine notifications: Registration of births (what is the legal requirement and what is recorded on a birth certificate)

A
  • legal UK requirement that all live births and still births (>24 weeks) are registered within 6 weeks
  • birth certificates record:
    1. baby name, date and place of birth, sex
    2. parent names, addresses, places of birth and occupations
    3. mothers maiden name
26
Q

Give example of 2 disease specific registers

A
  • National Cancer registration and analysis service
  • National Congenital Anomalies and Rare Diseases Registration Service

Both are administrated by National Disease Registration Service

27
Q

Strengths and weaknesses of disease specific registration services

A

STRENGTHS
- often nearly complete
- contain more detail than other routine data sources
- can be used to calculate disease incidence, prevalence and mortality
- Data is collected in a standardised way specific to the disease (ie cancer stage etc)

WEAKNESSES
- completeness is difficult to assess
- Completeness difficult to achieve if voluntary
- expensive to administrate
- time lag until information is available
- can only be used for conditions where diagnosis is very reliable

28
Q

National Cancer Registration and analysis services (what is it, what information is included, who provide information, who analyses information, what is information used for)

A
  • Information on entire cancer pathway for all patients diagnosed with concern in UK
  • Information includes
    1. Personal identifiers (to remove duplicates)
    2. Socioeconomic information
    3. type of cancer and stage
    4. treatment received
    5. Outcome
  • data is provided by all hospitals/ services involved in cancer care
  • data is assessed for completeness and analysed by national disease registration services
  • data is analysed by NDRS but also available for academic purposes on request
  • used for:
    1. looking at cancer incidence and outcomes over time
    2. comparing epidemiology and quality of care between areas
    3. epidemiological studies (ie association between cancer and CHD)
29
Q

Prescribing data: what can prescribing data be used for

A
  1. Monitoring compliance with guidelines
  2. Detecting aberrant or inappropriate clinical practice
  3. Identifying adverse reactions
  4. cost containment
  5. Addressing local priorities (ie decreasing CHD by increasing statin prescribing)
  6. Performance related payments (ie QoF)
30
Q

Prescribing data: Name a source of prescribing information and describe it and its limitations

A

ePACT2

  • online system run by NHSBA
  • provides information to organizations on prescriptions supplied which have been fulfilled in the community
  • gives information on:
    1. What has been prescribed by who
    2. costs and volumes of prescriptions
    3. savings that could be made through increased generic prescribing
    4. prescribing of high cost drugs

Only gives info on drugs dispensed in the community, not in hospital.
Only gives information on prescriptions that are fulfilled, not those issued but not fulfilled.
Cannot determine prescription appropriateness (this still requires interrogation on patient notes)

31
Q

Prescribing data: How to measure prescribing data, give some different units

A
  • there is a challenge on how to appropriately measure volumes and costs of prescriptions and how to meaningfully compare organisations

different units include:
1. Number of items
2. Quantity
3. Net ingredient cost
4. Actual cost
5. Defined daily dose
6. Defined daily quantity
7. Prescribing units (PUs)
8. ASTRO-PUs
9. STAR- PUs

32
Q

How to measure prescribing data: items (what is counted and what are the strengths/ limitations)

A
  • counts number of items on a script
  • simple and easy

however not always comparable if someone prescribes repeats monthly in smaller quantities they will have a much higher number of ‘items’ than someone who prescribes quarterly in larger quantities (even though the amount of drug is identical)

33
Q

How to measure prescribing data: quantity (what is counted and what are the strengths/ limitations)

A
  • number of tables/mls/mg etc
  • fairly simple
  • some mg/mls are not always comparable between medications ie one doctor may use a similar, more potent drug and therefore prescribe less total ‘mg’
34
Q

How to measure prescribing data: net ingredient cost (what is counted and what are the strengths/ limitations)

A
  • the is the basic price of a drug
  • normally used for comparing the costs of individual products
  • can be used for analysing volumes when drugs in a class are similarly priced at equivalent doses
  • however, there is normally large price differences
35
Q

How to measure prescribing data: actual cost (what is counted and what are the strengths/ limitations)

A
  • calculated by taking the net ingredient cost, subtracting the average national discount and adding an allowance for the container
  • normally used for comparing the costs of individual products
  • can be used for analysing volumes when drugs in a class are similarly priced at equivalent doses
  • however, there is normally large price differences
36
Q

How to measure prescribing data: defined daily dose (what is counted and what are the strengths/ limitations)

A
  • developed by WHO
  • aims to address the issue of comparing prescribing volumes
  • the daily defined dose is the average maintenance dose (per day) of a drug used for its main indication in an adult
  • allows drugs in the same broad therapeutic class to be added together and trends to be assessed or comparisons made between population groups
  • not appropriate for some types of drugs:
    1. one off doses ( its is for maintenance doses)
    2. topical preparations (issued in defined size and amount used depends on area covered etc)
    3. Contraceptives (vary in how many days are taken for)
    4. Combined medications
37
Q

How to measure prescribing data: Defined daily quantity (what is counted and what are the strengths/ limitations)

A
  • very similar to Defined daily doses but developed in UK
38
Q

How to measure prescribing data: prescribing units (what is counted and what are the strengths/ limitations)

A
  • prescribing costs/volumes vary between organisations not just because of prescribing practices but also because the of the population served
  • you therefore cannot simply compare average cost per patient
  • prescribin gunits try to make comparisons more meaningful
  • PUs take account of the fact that older patients require more medication
  • ie for each person >65 years on a GPs they count for 3 PUs, every person<65 years or a temporary resident count for 1 PUs. Amount spent on antidepressants per 1000 PUs can then be compared.
39
Q

How to measure prescribing data: ASTRO-PUs (what is counted and what are the strengths/ limitations)

A
  • like PUs but take account of more demographic data in addition to age

Age, sex and temporary resident orignated-PUs

40
Q

How to measure prescribing data: STAR-PUs (what is counted and what are the strengths/ limitations)

A
  • Units specific to different therapeutic areas which also take demographic factors into account

Specific therapeutic group, age-sex weighting’s related PUs

41
Q

Prescribing data: Define compliance

A

The extent to which a patients behaviour matches prescribers recommendations

Use of term is declining as it implies a lack of patient involvement

42
Q

Prescribing data: Define adherence

A
  • the extent to which a patients behaviour matches agreed recommendations from the prescriber
  • develops compliance definitions by emphasising the need for agreement between prescriber and patient
43
Q

Prescribing data: Define concordance

A
  • extent to which prescriber and patient agree on therapeutic decisions that incorporate their respective views
  • relatively new term used predominantly in UK
44
Q

Prescribing data: how is compliance measured, what are the weaknesses and name the 2 main measures

A
  • patient refill records are commonly used
  • these are relatively simple to access and objective
  • however does not give any info on whether drug is taken correctly (right time, right person)
  • there are 2 measures used:
  • medication possession ration
  • proportion of days covered
45
Q

Prescribing data: measuring compliance - medication possession ratio

A

days of medication supplied / days in period

  • criticised for often over estimating compliance since people often refill their scripts before they run out and often have a small stockpile of medication
  • you can therefore have a MPR>100%
46
Q

Prescribing data: measuring compliance - proportion of days covered

A

number of days in period ‘covered’/ number of days in period

  • only counts the days from when the last prescription would have run out. You cannot have a PDC >100%
47
Q

Pharmacovigilance: definition

A

Pharmacovigilance is the study of how to detect, assess, understand and prevent adverse effects of medication

Involves reducing harms associated with side effects of medications

48
Q

Pharmacovigilance: what processes are involved in pharmacovigilance

A

RIMM

1.RISK ASSESSMENT
- assessing the risk/benefits of treatment and deciding what action, if any, should be taken to improve safety

  1. INFORMATION SHARING
    - provide information to health professionals to optimise safe and effective use of medications
  2. MONITORING
    - monitoring the use of day to day medications to assess for previously unrecognised adverse effects of changing patterns of adverse effects
  3. MEASURING
    - measuring the impact of any action taken
49
Q

Pharmacovigilance: list information sources available

A
  1. YELLOW CARD SCHEME (administered by MHRA for clinicians to report suspected adverse reactions)
  2. BLACK TRIANGLE SYSTEM new drugs/ vaccines are marked with a black triangle in BNF. clinicians should report ALL suspected adverse effects for these (unlike other drugs where only serious or unusual reaction should be reported)
  3. DATA FROM STUDIES IN LITERATURE
  4. DATA FROM PHARMACEUTICAL COMPANIES
  5. DATA FROM WORLDWIDE REGULATORY AUTHORITIES
50
Q

Pharmacovigilance: what might be the outcomes for a drug as a result of pharmacovigilance processes

A
  1. Restrictions placed on drug use (ie cannot be used without ECG)
  2. change in legal status (from OTC to prescription only)
  3. Warning on medication information
  4. withdrawal
  5. change in dose
51
Q

Pharmacovigilance: What is the MHRAs role

A
  • coordinates yellow card scheme
  • communicates with health care professionals to warn about adverse effects through:
    1 changes to medicines summary product characteristics
    2. letters to clinicians for urgent warnings
    3. Regular drug safety bulletin
    4. safety alerts on website
52
Q

Data linkage: what is it

A
  • the process of linking information held in one data set to information held in a different data set
53
Q

Data linkage: methods- how is it done

A
  • simplest way is to use a unique identifier common to both data sets (ie NHS number)
  • if there is no common unique identifier can use other things such as name/ address/ DOB, however this is challenging when data is recorded differently and may be prone to errors

Fuzzy matching/probability matching- when records are linked based on commonalities across several fields which make it likely they are from the same person

54
Q

Data linkage: what can data linking be used for- give 3 examples

A
  • linking data sets can increase the amount of data available for analysis (widens the scope for epidemiological studies and statistical analysis)
  • examples:
  1. linking HES data to mortality data enables assessment of treatment outcome
  2. Linking health and education data allows assessment of impact of education on healtj
  3. Linking primary care and secondary care data allows assessment of patient care pathways
55
Q

Data linkage: give 5 limitations of data linkage

A
  • can be difficult if no common unique identifier has been used
  • can be challenging to accurately link historical data
  • can be challenging if data has been coded differently
  • patient may only have given consent for information to be used for a particular purpose
  • Can make data identifiable
56
Q

data linkage: give an example of data linkage

A
  • oxford data linkage study
  • 1963–>1999
  • linked data on hospital inpatient care with birth and death records in the oxford region

FPH exam (29 decks)

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