Surveillance of human disease: potentials and pitfalls

Dr Alex G Stewart (with help from Dr Sam Ghebrehewet and Dr Evdokia Dardamissis) Cheshire and Merseyside Health Protection Unit NWZG July 2012

Salmonella bareilly 2010

Farrington algorithm: no overall exceedance for Salmonella © Crown Copyright. All rights reserved. Health Protection Agency, 100016969 Regional Epidemiology Unit, North West. July, 2011 No Window

Surveillance: foundational

Public Health Function Wider workforce, healthy settings, policy development Population health protection Communicable diseases, Environment, Emergency Planning Public health response to cases of specific diseases Disease prevention (through immunisation) Nature of pathogens/hazards Microbiology, transmission, pathology Toxicology, haematology, environmental sciences HPA structures

Objectives of Surveillance

“If you can’t explain it simply, you don’t understand it well enough.” Albert Einstein (Physicist, 1879 –1955)

Objectives of surveillance

•

detecting acute changes (outbreaks / epidemics)

•

identifying & quantifying patterns (increased STIs)

•

observing changes in agents and hosts (‘Flu)

•

detecting changes in health practice (C Section)

•

disease investigation & control (meningitis)

•

health service planning (births, TB)

•

evaluation of prevention / controls (HIV in pregnancy)

•

study natural history / epi of disease (Cx cancer)

•

provide info & baseline data (eradication of measles)

Principles & Practice

“It is the mark of an educated mind to rest satisfied with the degree of precision which the nature of the subject admits and not to seek exactness where only an approximation is possible.” Aristotle (Philosopher, 384 –322 BC)

Epidemiological Surveillance

Definition:

 ‘Collection, collation & analysis of data & prompt dissemination of information to those who need to know so that

action

can result’ ( Langmuir, 1963 )

Action

further specified by CDC, Atlanta as ‘planning, implementation, and evaluation of public health practice’ To enable action, surveillance should be ‘ongoing, practicable, consistent, timely and have

sufficient

accuracy and completeness ’

(Comm Dis Ctrl Handbook, p246) Langmuir, A. 1963. The surveillance of communicable disease of national importance.

New England Journal of Medicine

268:182-192

Principles of surveillance

•

systematic collection of data

•

analysis of data to produce statistics

•

interpretation of statistics to provide intelligence

•

distribution of intelligence to those who will act

•

continuing surveillance to evaluate action

Sources

“You won’t be surprised that diseases are innumerable — count the cooks.” Seneca (Philosopher, 4 BC – 65 AD)

Communicable disease surveilance

1801 Census 1891 London (cholera diphtheria smallpox typhoid) 1899 E&W 1984 Public Health [Control of Disease] Act & associated regulations (Drs) 2008 Health and Social Care Act & associated regulations (HCW)

140000 120000

2012 Verbal reports accepted

100000 80000 60000 40000 20000 0 1913 1921 1929 1937 1945 1953 1961 1969 1977 1985 1993 2001

Diseases notifiable (to Local Authority Proper Officers) under the Health Protection (Notification) Regulations 2010 Acute encephalitis Acute meningitis Acute poliomyelitis Acute infectious hepatitis Anthrax Botulism Brucellosis Cholera Diphtheria Enteric fever (typhoid or paratyphoid) Food poisoning Haemolytic uraemic syndrome (HUS) Infectious bloody diarrhoea Invasive group A streptococcal disease & scarlet fever Legionnaires’ Disease Leprosy Malaria Measles Meningococcal septicaemia Mumps Plague Rabies Rubella SARS Smallpox Tetanus Tuberculosis Typhus Viral haemorrhagic fever (VHF) Whooping cough Yellow fever http://www.hpa.org.uk/Topics/InfectiousDiseases/InfectionsAZ/No tificationsOfInfectiousDiseases/ListOfNotifiableDiseases/

Sources of data:

Clinicians “Sentinel” General Practices Schools, Nursing / residential homes Local Health Protection Units Regional Units

National Centre for Infections

Laboratories Special surveys Maternity units Child health Departments in PCTs

“Enhanced” surveillance

Information from notifications & lab reports minimal: • name • address • disease/organism • onset (notification) More information collected on certain diseases • Tuberculosis • Meningococcal disease • Hepatitis B

Collection

“Not everything that counts can be counted, and not everything that can be counted counts.” Albert Einstein (Physicist, 1879 –1955)

Generic surveillance system

Wide dissemination

Policy makers PCTs/SHA Health practitioners

Supplementary data

Laboratory/ clinic

Database Specialist Laboratory Data Analysis

Types of Surveillance

Active

(outbreak, lab)

Passive

(normal)

Sentinel

(flu)

Based on secondary data analysis

(HES)

Collection – ensure: quality, uniformity & reliability

•

Definitions

(standard, specific, simple, acceptable, understandable) •

Ease of collection

(simple, clear, unambiguous, imp only) •

Timeliness

(pre specified: daily, weekly…) •

Completeness

(missing data) •

Motivation

(legal requirements / education incentives)

Problems

Advantages and disadvantages

Josiah Charles Stamp Economist, 1880 –1941

‘When you are a bit older’ a judge in India once told an eager young British civil servant, ‘you will not quote Indian statistics with that assurance. ‘The government is very keen on statistics—they collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. ‘But what you must never forget is that every one of those figures comes from the chowkidar, or village watchman, who just puts down what he damn pleases.’

Data collection problems

MORTALITY

• legally required • accuracy / limited outcome • not reflect incidence & prevalence • multiple causes • delays in data

Data collection problems

MORBIDITY

• legally required (<1984 fee ?prosecution) • professional duty (>2008) • good for severe & rare diseases • biased to acute infections • timeliness • under-notification of common diseases • over-notification due to inaccurate diagnosis • definitions

Data collection problems

LAB REPORTS

• accurate diagnosis • info on organisms & toxins easy but disease?

• not reflect incidence and prevalence • accuracy of test • limited epi info

Analysis & Interpretation

“If it looks like a duck, and quacks like a duck, we have at least to consider the possibility that we have a small aquatic bird of the family Anatidae on our hands.” Douglas Adams (Science fiction writer, 1952 –2001)

Analysis of data

•

Person –

age, sex, level of immunity, nutrition, lifestyle, occupation / school, hospitalisation, SES, risk factors, smoking alcohol… •

Place -

localised outbreaks, location or source of disease or person at time of infection, helps define risk groups (denominator) •

Time –

number reported / week; by season; long term trends

Annual measles notifications & vaccine coverage 1950 to 2000

Interpretation of data What’s going on Is change true?

800 600 400 200

Measles vaccine MMR vaccine

100 80 60 40 20

• Population changes (denominator)

0 1950 1960 1970 1980 1990 0 2000 Year

Source: Office for National Statistics and Department of Health • Improvement in diagnosis • Better awareness / reporting • Report duplication / change of system (case def.) • Context • Evaluate control measures

•

Identify new disease and infectious agents

Routine surveillance

: the reporting pyramid

(Wheeler JG et al, BMJ 1999; 318:1046-50) Acute, self-limiting, no mortality, common 1 reported to surveillance 1.4 positive lab result 6 stools submitted to the laboratory TB? Meningococcal disease? Ebola?

23 present to GP 136 cases of infectious intestinal illness in the community

Why is surveillance important?

Cases of Syphilis reported to GUM in the NW (males)

500 450 400 350 300 250 200 150 100 50 0 1999 2000

Cases of Syphilis reported to GUM in the NW (males by

2001 400 2002 2003 gay

reported orientation)

2004 2005 350 heterosexual 300 bisexual 250 200 150 100 50 0 1999 2000 2001 2002 2003

first year of diagnosis

2004 2005

Surveillance: Effectiveness of Interventions

Introduction of universal antenatal HIV testing in 1999

London

30% 25% 20% 15% 10% 5% 0% 4 2002 4 100% 90% 80% 70% 4 60% 50% 40% 30% 20% 10% 0% 1997 1998 1999 Year 2000 2001 Data for 2002 is preliminary - as the number of reports rise, estimates of infants becoming HIV-infected will fall. Proportion of infants exposed who become infected with HIV Proportion of HIV-infected pregnant women diagnosed before delivery 1

Actions

“The man who insists on seeing with perfect clearness before he decides, never decides.” Henri-Frederic Amiel (Philosopher, 1821 –1881)

Actions with intelligence

Communication, communication, communication!

Good & regular feedback to data collectors Regular reports:

With good distribution to interested & involved persons

Professionals

(newsletters, reports, journals)

Public

(prevention, diagnosis, treatment news)

Policy / decision makers

Evaluation of systems

“Life can only be understood backwards, but it must be lived forwards.” Soren Kierkegaard (Philosopher, 1813 –1855)

Evaluation of Epidemiological Surveillance systems

Is it •simple • flexible • acceptable • sensitive • representative • timely •

DID IT RESULT IN ACTION?

•

WHAT WAS DONE?

•

WHO DID IT?

http://www.ics.uci.edu/~eppstein/pix/dianafall04/mitch/Holes-m.jpg

Potentials

Develop analyses Olympics Improved links between systems animal surveillance Improved surveillance of chemical exposure non infectious incidents

“There are three kinds of epidemiologist: those who can count and those who can’t.” Anonymous ( adapted by John M. Cowden, Emerg Infect Dis. 2010 http://wwwnc.cdc.gov/eid/article/16/1/09-0030.htm

)