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An Introduction to Epidemiology pt 01
Dr Alex Keenan, Epidemiology and Surveillance Analyst, Cheshire & Merseyside HPU
27th April 2010
Learning Outcomes
• Basic Understanding of Epidemiology including analysis during outbreak situations
• Exercise 3
• Importance of Surveillance How? Why? What?
• Exercise 1
• Importance of Data including integrity, consistency, accuracy and limitations of data sources
• Exercise 2
Aims of Session 1
1. Understanding of Epidemiology
2. Importance of Surveillance
3. Surveillance Systems
4. Importance of Data Quality
5. Interpretation of Data
6. Data Sources
Definitions of Epidemiology
•Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems. A Dictionary of Epidemiology, Last J. (Ed.)
•Epidemiology is the study of factors affecting the health and illness of populations, and serves as the foundation of logic of interventions made in the interest of public health and preventive medicine. Wikipedia
Surveillance
Why Bother?
WHO recommended surveillance standards, Second editionhttp://www.who.int/csr/resources/publications/surveillance/WHO_CDS_CSR_ISR_99_2_EN/en/
The core functions in surveillance of any health event are:
Case Detection
Reporting
Investigation & Confirmation
Analysis & Interpretation
Action•Control / Response•Policy•Feedback
Surveillance – functions
Case definition
Agreed system/process
A means to follow up cases
Effective (secure) storage and skills to analyse
Political will to act – perceived importance
Requires…
Surveillance Systems
Database
Laboratory /clinic
Data AnalysisData Analysis
Policy makers
PCTs / LAs / SHAs
Health Practitioners
SpecialistSpecialistLaboratoryLaboratory
Dissemination
Supplementary data
Data Quality
Input
• Consistency • Data accuracy• Data reliability (integrity)
Output
• Standardised Outputs• Consistency of Outputs• Routine e.g. monthly, annual• Ad Hoc for outbreak situations e.g. swine flu
Weekly Surveillance Data
Campylobacter – Weekly Surveillance (1)
Campylobacter - Reports per Week
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Campylobacter – Weekly Surveillance (2)
Campylobacter - 4 weekly rolling average of reports
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Data Interpretation
Interrogating Datasets
• Define whether increase in reports or increase in cases
Compare with Previous Years (Temporal)
• Decide if increase is higher than expected
Compare Other Geographical Areas (Spatial)
• Is increase confined to one area
Compare Age Groups
• Is increase confined to one particular age group
Campylobacter – Weekly Surveillance (3)
Campylobacter - Central & Eastern Cheshire PCT
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Temporal Distribution
Mumps - 4 Weekly Rolling Average of ReportsCheshire & Merseyside 2005 - 2008
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Measles – Outbreak (1)
Measles – Outbreak (2)
Measles Cases by Age Groups (final)
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Probable Confirmed Possible Negative Equivocal
Exercise (1) - ~15 mins
Each group has a graph of data to interpret. For each one, assess: What is the data showing? What else would you like to know? What action might it inform? What are the strengths and weaknesses of the data?
Nominate someone to feed back from each group
Exercise (2) ~ 15 mins
What routine data sources do you think that you might use when studying an epidemiological issue?
• What sources are available?
• How is the data accessed?
• How easy is it to access the data?
• What is the timeliness of accessibility?
Hospital Utilisation Data
• Hospital Episode Statistics
• Presentation at Accident & Emergency
• Attendance at outpatients clinics
• Patients registered for Specialist Clinics
• Korner data
• Laboratory data
General Practice Data
General Practice Research Database
http://www.gprd.com/
• Established in 1987
• Largest computerised database of medical records in world
• Currently 450 PCT practices
• Records for 3.4 million patients (13 million total)
• 46 million patient years of validated data
• Includes data on demographics, symptoms, therapy, referrals and lifestyle factors
NHS Direct
• Organisation started with 3 pilot sites 1998
• National 2000
• Online 24 million visitors per year
• Phone service 7 million calls per year
• Interactive TV to 16 million households
• 2.5 million users per month
• Record sex, age, postcode, primary symptom, time and date of call
Other sources of Routine Data
• Cancer registries
http://www.ukacr.org.uk/
• Surveys
http://www.dh.gov.uk/PublicationsAndStatistics/PublishedSurvey/fs/en
• Mortality Figures
http://www.statistics.gov.uk/
• National Poisons Information Service
http://www.npis.org/
Routine Environmental Data
• Air Quality
http://www.airquality.co.uk/archive/index.php
• Pollution Inventory
http://www.environment-agency.gov.uk/maps/
• Radiation
http://www.hpa.org.uk/radiation/understand/radiation_topics/ultraviolet/uv_data/index.htm
• Contaminated Land
• Local Authority public registers
Congenital Abnormalities
European Surveillance of Congenital Abnormalities
• Started in 1979
• More than 1.5 million births surveyed per year in Europe ~ 29% of European Birth Population
• 43 registries in 20 European countries
• Structural defects
• Chromosomal abnormalities
• Inborn metabolism errors
• Hereditary diseases
http://www.eurocat.ulster.ac.uk/
Merseyside and Cheshire Congenital Anomaly Survey
• Started as foetal anomaly survey in 1992
• 1995 - Member of EUROCAT
• Approximately 1200 notifications of congenital anomalies per year
• Reporting voluntary
• Delivery within geographic area irrespective of place of residence
• ~ 27000 births each year
The key to successful analysis of routine data for epidemiological studies
•Good case definition
•Knowledge of the limitations of routine data
•Careful selection of non exposed population
•Care with use of small numbers
Session 02
Outbreaks, Clusters and some Maths
Aims of Session 2
• Outbreaks
• Clusters
• Studies
What is an outbreak?
• Observed number of cases greater than expected for a defined place and time period
• Two or more cases with a common exposure
• One case of serious/rare disease e.g. Ebola/plague/smallpox
How do clusters arise?
Human pattern recognition
Desire to explain things
Genuine clusters in time, space and person
Clusters can be…
In Timee.g. cases of legionella
In Place e.g. meningococcal cases in same school class
In Persone.g. cases of breast cancer in a family
The term cluster denotes the suspicion of an increased frequency of some event occurring, not that any increase has been demonstrated
How do outbreaks come to light?
• Acute/unusual event:
call to HPA, NHS or LA from a health professional, school, public, hotel staff, media etc.
• Routine surveillance:
data show an increase over the normal background level for the particular place and time of year
Types of outbreak
• Common source:
• Point – peak one IP after exposure• Intermittent – irregular pattern• Continuous – irregular pattern
• Propagated (person to person):
• Successive series of increasing peaks about one IP apart
• Mixture of the above
Figure. Measles cases by date of onset of rash. Region of Madrid, March 2006.(Cases reported until 16th March, 2006)
Example of propagated outbreak(see www.eurosurveillance.org/ew/2006/060330.asp)
Example of Point Source Outbreak
Epidemiological Curve Outbreak July 2009Point Source Outbreak
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Why investigate outbreaks?
• Control of disease
• Get new evidence about:
• optimal outbreak management• prevention of outbreaks• behaviour of novel organisms
• Political, legal or public concerns
How to use epidemiology in outbreaks
• Descriptive• Date of Onset – Epidemiological Curve• Age Groups• Sex
• Analytical• Single Variable Analysis e.g. Odds Ratios• Multi Variable Analysis
• Awareness of Bias e.g. Recall – collect evidence ASAP
• Agree Questionnaire before asking questions to avoid multiple calls
Examples of Types of Outbreak Investigation
• Case Control
• Matched• Unmatched
• Cohort
• All attendees
Risk Factors – some examples
• Age
• Sex
• All foods consumed
• Toilet Visited
• Foreign Travel
• Other restaurants / takeaways / parties attended
• Swimming Pools
• Farms Visited
Odds Ratios (I)
The odds ratio is a calculation that is used to measure the strength of a relationship between 2 variables.
Odds Ratio (Cross Product) =
Ate / Exposed Didn’t Eat / Not Exposed
Ill / Disease a b
Not ill / No Disease
c d
bc
ad
Odds Ratios Example
During a Wedding people became ill and we tried to ascertain if there was a link to any particular food.
Odds Ratio (Egg Salad) = = = 12
Egg Salad Ate Didn’t Eat
Ill 8 1
Not ill 1 4
bc
ad11
48
Exercise (3) ~ 30 mins
Each group has a table of data to interpret. For each one, assess: What is the data showing? What action might it inform?
Then analyse the data as you see fit
Nominate someone to feed back from each group
Outbreak at an event in Liverpool July 2009
• 1000 - 1200 Attendees at event
• Several Reports of illness associated with people who attended event and ate food from event
• Questionnaires returned and completed by 200 people
• 148 of those were ill
• Unmatched Case Control Study
Investigation
• Environmental
• Environmental Health Officers visited premises where food was prepared to investigate possible issues
• Samples taken from premises
• Microbiological
• Samples taken from premises analysed• Stool samples analysed from those who were ill
• Epidemiological
• Epidemiological Curve• Identify Risk Factors• Perform Odds Analysis• Further more detailed analysis
Epidemiological Curve
Epidemiological Curve Outbreak July 2009
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Risk Factors Identified
• Age
• Sex
• Spring Rolls
• Chicken Lollipop
• Beef Cutlet
• Vegetable Samosa
• Chicken Curry
• Rice
Single Variable Analysis
Odds Ratio Confidence Interval
p – value
Age 1.02 0.99 – 1.04 0.056
Sex 1.30 0.71 – 2.67 0.34
Spring Roll 4.87 2.60 – 9.77 <0.01
Chicken Lollipop
6.05 2.80 – 10.52 <0.01
Beef Cutlet 6.21 3.12 – 12.56 <0.01
Vegetable Samosa
3.61 1.99 – 7.22 <0.01
Chicken Curry 15.40 6.99 – 31.84 <0.01
Rice 15.70 6.45 – 32.47 <0.01
Further Analysis
• More detailed analysis involving (stepwise) multivariate logistic regression
• People Ate more than 1 food so can take into account several foods eaten
• Bias – people from same household likely to all reply or not reply
Multivariate Analysis
Odds Ratio Confidence Interval
p- value
Chicken Curry 3.63 1.54 – 8.51 0.003
Rice 12.63 4.34 – 36.88 <0.0001
Summary of Epidemiological Findings
• Unmatched case control analysis performed
• Point source outbreak
• Age not a risk factor
• Sex not a risk factor
• All foods identified as risk factors during single variable analysis
• Chicken Curry and Rice identified as most likely risk factors to be associated with illness
A Selection of Useful Reference Books
• A Dictionary of Epidemiology, Last J. (Ed.)
• Research Methods in Health, Bowling A.
• A – Z of Medical Statistics a companion for critical appraisal, Pereira-Maxwell F.
• Essential Public Health, Donaldson & Donaldson
• Oxford Handbook of Public Health Practice, Pencheon D. et. al. (Eds.)