geo data institute, university of southampton detailed practical models data analysis and modelling...

Geo Data Institute, University of Southampton

Detailed Practical Models

Data Analysis and Modelling

With an emphasis on

Care of People with Particular Diseases and Conditions

and for

Planning and Managing Health Services


Collaborative work

by

Universities, Industry, and Service Organisations

for

Developing, testing and validating practical detailed models

is

mutually beneficial

Main Messages


Databases +

Data analysis including classifications +

Detailed models at the level of individual entities=

Powerful tools for making good

Planning and managing decisions

Main Messages

Personal Information

Arjan Shahani

1956 – 1963 Statistician with Philips, London

1964 – 1967 Bristol College of Technology/Bath University

Since 1967 University of Southampton.•Developed Operational Research and collaborative work •Evolved Health work since about 1980.•Set up Institute of Modelling for Healthcare in 1987 in Mathematics Department. •Retired from full-time formal university work in 1998.•Set up Health Data Analysis and Modelling Group in the GeoData Institute, University of Southampton•Health work continues


Collaborative Work from Southampton • Collaborative work through MSc projects, PhD projects, and Commissioned research projects with many organisations ExamplesDesigning a new sea port for container trafficForecasting milk productionPassenger flows through an airport Interactions between research and production for new products Flows of vegetables through an Indian MarketPlanning for batch production

Care of people with particular diseases

Planning and managing capacities for health services

Health Work At and Through Southampton

Since 1980•Courses on Health work in Southampton MSc OR.

•About 25 PhDs. About 100 MSc Projects.

•About £500K obtained for funding Research Assistants

for work on Health Care and Health Services.

•Research work of the Operational Research Group

awarded Grade 5 in a national UK evaluation exercise in

1996. Health work made a highly significant impact in

this evaluation. OR Group was the only one in Maths

Dept. to be awarded this top Grade.


Collaborative Health Work

• Collaborative work with many Health professionals and

organisations in UK, Austria, Brazil, India, and Sweden.

Nature of the Southampton Models

• Based on individual patients grouped in appropriate classes.

• Uncertainty and variability are taken into account PROPERLY.

• Models can be easily used by health professionals

Examples

• Care of people with diabetes

• Critical Care capacities

• Whole hospital capacities


A Program for Classification and Data Analysis

• Rapid extraction of information from databases.

• Patient flows

• Patient outcomes

• Use of resources

• Auditing and monitoring• Links with detailed health care and health services models

• Demonstration of PORT

Health Care Modelling

•Understand and represent the PROCESSES involved in disease, care, patient flows etc.

•Description of community or patient groups. e.g. age, sex, risk groups •Disease history or patient progress

•Interventions e.g. screening, vaccination, treatment, socio- economic actions

•Resources needed or planned

•Costs of resources


Example of Health Care Modelling

Data Analysis and Modelling

for

Early Detection and Treatment of Breast Cancer

Breast Cancer

Breast cancer is a major form of cancer in many countries.

•In the UK breast cancer results in:•Annually 27,000 new registrations and 15,000 deaths in England and Wales.•20% of all female cancer deaths.•5% of total female deaths.•High mortality in women aged 35-54 years.•In UK women between the ages of 50 and 65 are invited for screening at three-yearly intervals.


Early Detection of Breast cancer

•Delay in detection can be reduced by screening.

•Apparently healthy women can be screened through:

•Self-examination. •Clinical examination. •X-rays (mammography).

Evidence from many countries suggests that early detection reduces mortality.

Screening using mammography is available in a number of countries.

Cancer starts

Delay indetection

Woman becomes aware Visit to doctor

Treatments start

Time


Treatment of Breast Cancer

•Many are treatments available.•Treatment depends on the severity of cancer.

Stage I: Small moveable tumour in breast only.

Stage II:Tumour not advanced but spread to lymph nodes.

Stage III:Locally advanced tumour. May be attached to chest muscles.

Stage IV:Distant metastases.

•Mortality rates are the usual measures of goodness of treatment.•Mortality rates vary between hospitals and between countries.•UK mortality rates are high in comparison with Western Europe and

USA.

Modelling

Evaluation without formal cancer models

Mortality Statistics

Intervention Evaluation

Screening or Treatments

eg Randomised Controlled Trial (RCT) for annual mammography

•Noisy data. •Expensive field trials •Information about annual screens •Other options need new RCT

•Cancer models + data from various sources including any necessary RCT is a powerful combination


Modelling of Screening for Breast Cancer

• Three PhD theses: V de Senna, Y Ouinten, S Mandurah

• Models given to UK Department of Health

• Current UK screening policy was one of the options evaluated by the Southampton Models


Data and Information for Screening Model

∙Cancer Register ∙Life Tables

∙Population ∙Tumour Growth rate

∙Age of Onset of Cancer ∙Screening Processes

∙Tumour Size ∙Error probabilities


Screening Model

Inputs to Model Outputs from Model

Particular data sets Tumour size

Expert knowledge Aggressiveness

Screening options Age at detection

eg Starting age Stage of cancer

Intervals % detected by screens

Type of test Resources & costs

Etc


Illustrative Results From Screening Model

Stage at registration

Non-Screened

319%

113%

260%

48%

2 year Mammography

159%

230%

44%

37%

3 year Mammography

153%2

35%

4 4%3

8%


Illustrative Results From Screening Model

Tumour doubling time (measure of cancer aggressiveness)

Treatment Model

•Very general model that can be tuned for different users.

•Users define treatment for different stages of cancer, patient progress and treatment outcomes.

Stage 2

Stage 1

Stage 3

Stage 4

Treatment

Treatment

Treatment

DiseaseFree

NoResponse

Response

ProgressiveDisease

Local

Distant

Local andDistant

TreatmentDeathFromBreastCancer

Death FromOther Causes

Illustrative Results From Treatment Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Survival Time (Years)

Pro

babi

lity

Stage 1 Stage 2 Stage 3 Stage 4

Survival by stage at diagnosis.

Illustrative Results From Treatment Model

Survival by age at diagnosis.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Survival Time (Years)

Pro

babi

lity

30-39 40-49 50-59 60-69


Particular Diseases

Asthma, Breast Cancer, Cataracts,

Cervical Cancer, Chlamydial Infection,

Colorectal Cancer, Depressive Illness,

Diabetes, HIV/AIDS, Trachoma

Some Southampton Health Care Models

Arrival of Individual patient. Patient type. Care Unit needed

Admission rules for Care Units

Required capacities available?

Send elsewhere.Evaluate increased

capacities?

No Yes

Admit Treat Discharge

Health Services Models Capture Patient Flows and Use of Resources

Example: Planning a New Respiratory Unit

Days in a year

No.ofbeds

Proposed capacity of 30 beds

No. of beds in use from a detailed model


Example:Planning for a Group of Hospitals

• Outpatients, Inpatients, Day cases at four hospitals

• Detailed models at the level of individual patients grouped within about 20 specialities

• Models provided detailed quantitative information about

• The number of beds for each speciality

• The number of operating theatres

• Organisation of outpatient clinics taking the travelling times of the patients into account


Critical Care Capacities

• Demonstration of a model for planning Critical Care Capacities

• The computer solution of the model is called CCU-SIM

• CCU-SIM can be tuned to represent a particular Critical Care Unit

Comparing Data with Model Predictions

Data Model

Mean

Model 95%

CL for Mean

Emergency Admissions 822 821 814, 828

Elective Admissions 190 191 187, 195

Total Admissions 1012 1012 1004, 1020

Number of Deaths 184 185 182, 188

Bed Occupancy 80.0% 80.3% 79.3%, 81.3%

Deferral rate 20% 21.5% 20.5%, 22.5%

Transfer Rate 1% 1.2% 1.0%, 1.4%


Distribution of Beds Occupied

• Note evidence of pressure due to heavy use of capacity


Planning Critical Care Capacities

Evaluation of Scenarios

• Many scenarios involving changes in• Capacities• Case-mix and numbers of patients• Organisation of Critical Care Units

were evaluated

• Some examples will illustrate the use of Southampton models

Increased Demand and Increased Beds

(Base case has 13 beds) Base

Case

14

Beds

16

Beds

Emergency Admissions 822 819 1047

Elective Admissions 190 190 234

Total Admissions 1012 1009 1281

Number of Deaths 184 186 236

Bed Occupancy 80.0% 74.2% 81.8%Deferral rate 20% 12.6% 19.7%

Transfer Rate 1% 0.6% 1.2%

No Neuro Surgery Patients


Data 13 Beds 12 Beds




Number Died 176 175 179

Bed Occupancy 74.4% 80.8%

Deferral Rate 15.5% 25.3%

Transfer Rate 0.7% 1.3%

20 Extra Planned General Surgery Patients


Data 13 Beds

14 Beds




Number Died 186 185

Bed Occupancy 81.8% 76.2%

Deferral Rate 23.0% 15.1%

Transfer Rate 1.3% 0.6%


Hospital Capacities

Demonstration of a model

for

Planning Hospital capacities

Work with Hospitals

Brazil: Through Rio de Janeiro University

India : G.T., Mumbai. KEM, Pune

Sweden: Critical Care (Prof Walther, Dr Steins)

UK: Basingstoke, Bournemouth, Chichester, Dumfries, Heatherwood, Isle of Wight, Norwich, Poole, Princess Anne, Queen Alexandra,

Royal Berkshire, Salisbury, Southampton General, St Marys, Tunbridge, Winchester, + 7 others for Critical Care capacities


Hospital Capacities: Critical Care Units. A&E + MAU. Hospital Care units. Hospital (existing or new) as a whole.

Outpatient Clinics: Orthopaedics, Depressive illness, ENT, Eye, Skin.

Waiting Lists: Inpatients and Outpatients.

Regional Capacities: Cleft lip and Palate, Coronary, Dental.

Service Provision: Maternity Care.

“Whole System”: Primary Care, Acute Hospital, Post-Acute Care.

Forecasts of daily emergency admissions for hospitals in England. Met Office project

Some Southampton Health Services Models


Collaborative Work

• MSc projects

Southampton (England) Students June to September Linkoping (Sweden) Students September to December

• PhD / MPhil projects: one to three years

• Research grants

• Commissioned work

• Plan for work with Indian Institutes


Collaborative Operational Research in Health A Meeting in India

• Indian Health Professionals and Operational Researchers

• Interested people from other countries

• Meeting in January 2007

• Visiting Mumbai, Delhi, Varanasi, Pune, Coimbatore in December 2005 for planning the meeting


Collaborative Operational Research in Health A Meeting in India

Areas to be covered

• Infectious diseases

• Chronic conditions

• Planning and managing health capacities

• Indian System of Medicine (Ayurveda)

• Combining Indian and “Western” Systems of Medicine


Contact Details

Dr Arjan Shahani,

Director,

Health Data Analysis and Modelling Group,

GeoData Institute,

University of Southampton, Southampton SO15 7PJ

[email protected]

[email protected]

[email protected]

Email to hotmail address with copies to

geodata and maths addresses please

geo data institute, university of southampton detailed practical models data analysis and modelling...

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