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Findings and implications of the Global Burden of Disease Study 2010
Royal Society, London, 14 December 2012
Professor Theo Vos
School of Population Health
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Welfare Reform Consultation Survey - People No Longer in EmploymentMark
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- Over 50% of respondents were advised or forced to leave their previous employment due to health reasons.
- The greatest reported barriers to gaining employment were difficulty with cognition, chronic pain, fluctuating health, and overwhelming exhaustion.
- While most respondents would like to work, they reported needing accommodations such as flexible hours, working from home, and frequent breaks in order to do so.
Proximo Consulting Services is an IT consulting firm founded in 1997. It specializes in data mining and modeling to help clients gain insights from their data. Proximo has 20 employees and offices in several major cities. It serves a variety of industries including consumer products, health, finance, and travel. Proximo's proprietary software, GMAX, uses genetic programming to automatically test combinations of data variables and identify powerful predictors.
1) The document discusses multiplication and division facts and operations. It provides a multiplication/division facts table and examples of multiplication diagrams and shortcuts to help children learn these concepts.
2) Vocabulary terms are defined, including product, factor, and quotient. Do-anytime activities and games are suggested to practice the concepts, such as using pennies to demonstrate the commutative property of multiplication.
3) Answers to sample homework problems are provided as a guide for parents to check their child's work.
The document discusses finding required versus optional arguments in PropBank. It analyzes preposition phrases that occur with numbered arguments and identifies the most frequent prepositions. It also uses pointwise mutual information and normalized pointwise mutual information to distinguish required from optional preposition phrase arguments. The results show the distribution of prepositions for different numbered arguments. Future work is proposed to identify required arguments based on verb sense and syntactic construction.
Combining metric and qualitative approach in a measure of similarity for ill-...Célia M. D. Sales
Sales, C. M. D., & Wakker, P. P. (2010). Combining metric and qualitative approach in a measure of similarity for ill-structured data sets. Paper presented at the XVII Meeting of the Portuguese Association of Data Classification and Analysis JOCLAD, Lisbon.
Findings and implications of the Global Burden of Disease Study 2010
Royal Society, London, 14 December 2012
Professor Theo Vos
School of Population Health
This document contains a biology exam paper with two questions. Question 1 asks students to analyze data from an experiment testing the hypothesis that the distribution of dogwhelk snails is determined by the availability of their main food source, barnacles. Students are asked to: (1) estimate total barnacle numbers in sampling areas and present the data in a table, (2) present the data graphically, and (3) calculate a rank correlation coefficient and draw a conclusion about the relationship between dogwhelk and barnacle numbers based on a critical value. The document provides instructions to candidates and information about marking.
Welfare Reform Consultation Survey - People No Longer in EmploymentMark
The document is a survey about employment status and barriers to employment for people with M.E. (myalgic encephalomyelitis). Key findings from the survey include:
- Over 50% of respondents were advised or forced to leave their previous employment due to health reasons.
- The greatest reported barriers to gaining employment were difficulty with cognition, chronic pain, fluctuating health, and overwhelming exhaustion.
- While most respondents would like to work, they reported needing accommodations such as flexible hours, working from home, and frequent breaks in order to do so.
Proximo Consulting Services is an IT consulting firm founded in 1997. It specializes in data mining and modeling to help clients gain insights from their data. Proximo has 20 employees and offices in several major cities. It serves a variety of industries including consumer products, health, finance, and travel. Proximo's proprietary software, GMAX, uses genetic programming to automatically test combinations of data variables and identify powerful predictors.
1) The document discusses multiplication and division facts and operations. It provides a multiplication/division facts table and examples of multiplication diagrams and shortcuts to help children learn these concepts.
2) Vocabulary terms are defined, including product, factor, and quotient. Do-anytime activities and games are suggested to practice the concepts, such as using pennies to demonstrate the commutative property of multiplication.
3) Answers to sample homework problems are provided as a guide for parents to check their child's work.
The document discusses finding required versus optional arguments in PropBank. It analyzes preposition phrases that occur with numbered arguments and identifies the most frequent prepositions. It also uses pointwise mutual information and normalized pointwise mutual information to distinguish required from optional preposition phrase arguments. The results show the distribution of prepositions for different numbered arguments. Future work is proposed to identify required arguments based on verb sense and syntactic construction.
Combining metric and qualitative approach in a measure of similarity for ill-...Célia M. D. Sales
Sales, C. M. D., & Wakker, P. P. (2010). Combining metric and qualitative approach in a measure of similarity for ill-structured data sets. Paper presented at the XVII Meeting of the Portuguese Association of Data Classification and Analysis JOCLAD, Lisbon.
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3. Case Finding
• NHS predictive models
• Models for social care
Evaluation
Remuneration
4. Why Predictive Modelling?
• BMJ in paper* in 2002 showed Kaiser Permanente in
California seemed to provide higher quality healthcare
than the NHS at a lower cost
*Getting more for their dollar: a comparison of the NHS with California's Kaiser Permanente BMJ 2002;324:135-143
• Kaiser identify high risk people in their population and
manage them intensively to avoid admissions
• Inaccurate Approaches:
– Clinician referrals
– Threshold approach (e.g. all patients aged >65 with 2+
admissions)
5. Frequently-admitted patients
50
Average number of emergency bed days
45
40
35
30
25
20
15
10
5
0
-5 -4 -3 -2 -1 Intense +1 +2 +3 +4
year
6. Regression to the mean
Average number of emergency bed days
50
45
40
35
30
25
20
15
10
5
0
Intense
-5 -4 -3 -2 -1 year +1 +2 +3 +4
7. Emerging Risk
50
45
Average number of emergency bed days
40
35
30
25
20
15
10
5
0
-5 -4 -3 -2 -1 +1 +2 +3 +4
Intense
year
8. Kaiser Pyramid
Small numbers of
people at very high
risk
The Pyramid
represents the
distribution of
risk across the
population
Large numbers
of people at
low risk
[Size of shape is proportional to number of patients]
9. Patterns in routine data
Inpatient
Inpatient A&E data
A&E data GP Practice
GP Practice
data
data data
data
Outpatient
Outpatient
data
data PARR
Combined
Model
Census
Census
data
data
10. Scotland Wales
• SPARRA • PRISM model
• SPARRA-MD • Welsh Predictive Risk
Service
11. Name, Address, DOB 131178 J7KA42
Encrypted,
linked data
Inpatient
Inpatient
Name, Address, DOB 131178 J7KA42
Outpatient
Outpatient
J7KA42
A&E
A&E
Name, Address, DOB 131178 J7KA42
GP
GP
Name, Address, DOB 131178 J7KA42
J7KA42 76.4
131178 76.4
Decrypted data
with risk score
attached
12. 10 Million Patient-Years
10 Million Patient-Years
of Data
of Data
Development Validation
5 Million Patient-Years
5 Million Patient-Years 5 Million Patient-Years
5 Million Patient-Years
of Data
of Data of Data
of Data
13. Inpatient
Inpatient
Outpatient
Outpatient Development
A&E
A&E
GP
Sample
GP
J7KA42 J7KA42 J7KA42
YH8TPP YH8TPP YH8TPP
G8HE9F G8HE9F G8HE9F
3LWZ67 3LWZ67 3LWZ67
2NX632 2NX632 2NX632
LG5DSD LG5DSD LG5DSD
3V9D54R 3V9D54R 3V9D54R
Year 1 Year 2 Year 3
14. Inpatient
Inpatient
Outpatient
Outpatient Development
A&E
A&E
GP
Sample
GP
J7KA42 J7KA42 J7KA42
YH8TPP YH8TPP YH8TPP
G8HE9F G8HE9F G8HE9F
3LWZ67 3LWZ67 3LWZ67
2NX632 2NX632 2NX632
LG5DSD LG5DSD LG5DSD
3V9D54R 3V9D54R 3V9D54R
Year 1 Year 2 Year 3
15. Inpatient
Inpatient
Outpatient
Development
Outpatient
A&E
A&E Sample
GP
GP
J7KA42 J7KA42 J7KA42
YH8TPP YH8TPP YH8TPP
G8HE9F G8HE9F G8HE9F
3LWZ67 3LWZ67 3LWZ67
2NX632 2NX632 2NX632
LG5DSD LG5DSD LG5DSD
3V9D54R 3V9D54R 3V9D54R
Year 1 Year 2 Year 3
16. Inpatient
Inpatient
Outpatient
Validation
Outpatient
A&E
A&E Sample True
False
Positive
Negative
GP
GP
A89KP5 A89KP5 A89KP5
833TY6 833TY6 833TY6
I9QA44 I9QA44 I9QA44
85H3D 85H3D 85H3D
6445JX 6445JX 6445JX
233UMB 233UMB 233UMB
False
Positive
RF02UH RF02UH RF02UH
True
Negative
Year 1 Year 2 Year 3
17. Inpatient
Inpatient
Outpatient
Outpatient
Using the Model
A&E
A&E
GP
GP
A89KP5 A89KP5
833TY6 833TY6
I9QA44 I9QA44
85H3D 85H3D
6445JX 6445JX
233UMB 233UMB
RF02UH RF02UH
Last Year This Year Next Year
18. Distribution of Future Utilisation
£4,500
Actual Average cost per patient
£4,000
£3,500
£3,000
£2,500
£2,000
£1,500
£1,000
£500
£0
0 10 20 30 40 50 60 70 80 90
Predicted Risk (centile rank)
23. How the output of predictive
models are used
• Case Management
• Intensive Disease Management
• Less Intensive Disease
Management
• Wellness Programmes
Potential Misuses
Dumping
Cream-skimming
Skimping
24. Health Needs Social Care Needs
• Diagnoses • Client group
• Prescriptions • Disabilities
• Record of Health • Record of care
Contacts history
PAST Predictive
Model
FUTURE
Health Service Use Social Care Use
• GP visits • Residential care
• Community care • Intensive home
• Hospital care care
• Direct payments
32. Person-Based Resource Allocation
• Historically, GP practice budgets set on area-
based variables
• New approach is person-based
• Exclude certain variables to avoid perverse
incentives
– Procedures
– Disease severity
35. Trend
Model
Cost
predicts:
Details Model predicts
which patients
will become
high-cost over
next 6 or 12
months
Examples Low-cost
patient this
year will
become high-
cost next year
36. Trend
Model
Cost Event
predicts:
Details Model predicts Model predicts
which patients which patients
will become will have an
high-cost over event that can
next 6 or 12 be avoided
months
Examples Low-cost Patient will be
patient this hospitalized
year will
become high- Patient will
cost next year have diabetic
ketoacidosis
37. Trend
Model
Cost Event Actionability
predicts:
Details Model predicts Model predicts Model predicts
which patients which patients which patients
will become will have an have features
high-cost over event that can that can readily
next 6 or 12 be avoided be changed
months
Examples Low-cost Patient will be Patient has
patient this hospitalized angina but is
year will not taking
become high- Patient will aspirin
cost next year have diabetic Patient does
ketoacidosis not have
pancreatic
cancer
(Ambulatory
Care Sensitive)
38. Trend
Model
Cost Event Actionability Readiness to
predicts: engage
Details Model predicts Model predicts Model predicts Model predicts
which patients which patients which patients which patients
will become will have an have features are most likely
high-cost over event that can that can readily to engage in
next 6 or 12 be avoided be changed upstream care
months
Examples Low-cost Patient will be Patient has Patient does
patient this hospitalized angina but is not abuse
year will not taking alcohol
become high- Patient will aspirin
cost next year have diabetic Patient does Patient has no
ketoacidosis not have mental illness
pancreatic
cancer
(Ambulatory Patient
Care Sensitive) previously
compliant
39. Trend
Model
Cost Event Actionability Readiness to Receptivity
predicts: engage
Details Model predicts Model predicts Model predicts Model predicts Model predicts
which patients which patients which patients which patients what mode and
will become will have an have features are most likely form of
high-cost over event that can that can readily to engage in intervention
next 6 or 12 be avoided be changed upstream care will be most
months successful for
each patient
Examples Low-cost Patient will be Patient has Patient does Patient prefers
patient this hospitalized angina but is not abuse email rather
year will not taking alcohol than telephone
become high- Patient will aspirin
cost next year have diabetic Patient does Patient has no Patient prefers
ketoacidosis not have mental illness male voice
pancreatic rather than
cancer female
(Ambulatory Patient
Care Sensitive) previously
compliant Readiness to
change