Infectious Disease Epidemiology How do we use...
Transcript of Infectious Disease Epidemiology How do we use...
Infectious Disease Epidemiology How do we use statistics?
Laura MacDougall
BC Centre for Disease Control SFU, Stat 305, April 5, 2012
Epidemiology
• “The study of the distribution and determinants of disease in (human) populations; its application to control health problems” Last, 2001
• Components of ID epidemiology – Surveillance – Outbreak investigation – Research
Surveillance
Surveillance framework Collection, analysis, interpretation and dissemination of data that leads to action to prevent and control disease
Collection
Analysis
Interpretation Dissemination
Action
Data sources • Reportable diseases • Laboratories • Administrative data
– Physician billings – Hospitalisations
• Clinical data – Clinics or emergency
dpt • Vital statistics
– Births – Deaths
• Hospitals, health facilities
• Schools, daycares, workplace
• Surveys • Immunization
registries • Vaccine-associated
adverse reaction data
Data collected • Person
– Demographics (age, sex, ethnicity) – Clinical (symptoms, lab data)
• Place – Residence – Exposure
• Time – Onset→specimen collection→diagnosis →reported
• Exposures/risk factors – Travel, food history, sexual practices, immunization
history…
Automatic aberration detection
• Automatic detection of statistically significant change in distribution of disease (i.e. outbreak)
• Methods – Identify electronic retrospective data to forecast
expected numbers – Select a method (e.g. time series, regression,
CUSUM) to adjust for trends, seasons, past outbreaks and develop model
– Determine alert threshold – Apply model to prospective data – Identify and interpret signals detected
Automatic aberration detection example
BCCDC 2007
Outbreak Response
A family cluster is detected…
• Onset February 20/03 • 5/6 symptomatic
– 2 hospitalized – 3 lab confirmed
• Food history: – Frozen chicken
nuggets – French toast
An outbreak?
Incidence of S. Heidelberg in 2003 vs. 5 year average (1998-2002 )
0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11 12
5 YR AVE 2003
No.
cas
es
Week
Methods
• Case-control study – Cases
• Lab confirmed January 1 – April 1, 2003 • 10 cases only ; 1 case per household • Recall period – 7 days prior to onset
– Controls • 1:1 age matched: 0-6, 7-17, 18+ • Forward digit dialing • Recall period – 7 days prior to interview
Risk Factors
*For one case, no information on strip exposure was provided.
Exposure MOR 95% CI # cases exposed*
# controls exposed
Nuggets alone 2 0.29 < OR < 22.11 4/20 4/18Strips alone undefined 6/19 0/18Nuggets + strips 8 1.07 < OR < 354.98 13/19 4/18Nuggets + strips (excl. restaurant i.e. McDonalds)
11 1.60 < OR < 473.47 13/19 1/18
No other risk factor significant
L. MacDougall, M. Fyfe et al. J Food Prot. 2004 Jun;67(6):1111-5
Consumer attitudes + beliefs
• Considered product pre-cooked – 1/3 cases, 1/3 controls
• 27% always or sometimes used microwave
• 35% washed hands less frequently after handling nuggets vs. raw chicken
• 37% re-packaged their strips – Of these, only 26% kept cooking instructions
L. MacDougall, M. Fyfe et al. J Food Prot. 2004 Jun;67(6):1111-5
Conclusion
• Frozen chicken nuggets/strips are a risk factor for S. Heidelberg infection
• Contributes significantly to the baseline (non-outbreak) rate of infection in BC
• Labels must be changed to clearly identify nuggets/strips as a raw product
• Consumer education required
Actions taken
• Local press release to consumers – CFIA and BCCDC
• Labelling changes! Before
After
Research
A Cryptic Story
5
• An increase in the number of animal and human cryptococcosis noted in 2001
• Clinical symptoms: prolonged cough, sharp chest pain, unexplained shortness of breath, severe headache, fever, night sweats, weight loss; skin lesions (animals)
• Profiles of human cases did not fit the traditional understanding of cryptococcosis
• All cases resided on or had visited Vancouver Island prior to the onset of illness
Emergence of C. gattii on VI
Fyfe CCDR 2008
Hospitalizations for cryptococcal infection in HIV-negative patients by location of treatment, BC 1995-2004
0
5
10
15
20
25
30
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
MainlandIsland
(N=158)
Fyfe CCDR 2008
Cryptococcus isolates by serotype, BC, 1987-2000
0
2
4
6
8
10
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Num
ber o
f iso
late
s
BDA
(N=36)
Emergence of C. gattii on VI
Risk factors? • Case control study
– 30 cases from 1999-2001 – Matched to 2 controls by sex – Controls required recent normal CXR – Standard questionnaire administered
• Population control study – 218 cases from 1999-2007 – Compared to population data
• Age and sex distribution (BC Statistics) • Smoking prevalence (Canadian Community Health Survey) • Cancer prevalence (BC Cancer Agency) • HIV, COPD prevalence (published data)
MacDougall EID, 2011 Feb;17(2):193-9
Risk factors (results) Risk factor Case control study
mOR (95%CI) Case vs. population control
prevalence (p-value)
Age > 50y Not significant 72.4% vs. 31.3% (p<0.001)
Male sex Not evaluated 55.8% vs. 49.6% (p=0.198)
Smoking 1.18 (0.44-3.20) 41.9% vs. 17.8% (p<0.001)
Systemic steroid use 8.1 (1.7-37.8) Not evaluated
Chronic lung disease 3.2 (1.1-9.5) 4.1% vs. 8.0% (p=0.090)
Cancer 2.0 (0.6-6.1) 24.7% vs. 3.6% (p<0.001)
HIV infection Not evaluated 3.7% vs 0.2% (p<0.001)
MacDougall EID, 2011 Feb;17(2):193-9
Study Objective
To delineate the geographic areas where Cryptococcus gattii is currently established and forecast areas that could support Cryptococcus gattii in the future for targeted public health messaging of Cryptococcus gattii risk and prioritization of environmental sampling
S. Mak, MSc thesis, 2009
GARP Animal
Human
Environmental
Elevation
Aspect
Slope
Biogeoclimatic
January Temp (x3)
July Temp (x3)
Precipitation (x3)
Soil (x2)
Determine significant variables
Methodology and Data
Resulting models
S. Mak, MSc thesis, 2009
S. Mak, MSc thesis, 2009
S. Mak, MSc thesis, 2009
S. Mak, MSc thesis, 2009
Conclusion (1)
• Suitable ecological niche for Cryptococcus gattii is
available on the BC mainland
• Cryptococcus gattii distribution in BC associated with
areas having >1oC January average temperature and
<770m elevation (mean = 100m)
• Animal distribution of cryptococcosis corresponds
directly with human distribution
S. Mak, MSc thesis, 2009
Conclusion (2)
• Ecological niche modeling of Cryptococcus gattii
produced very accurate predictions (>98% accuracy)
• The ecological niche model based on environmental
sampling data produced the most conservative forecast
• Environmental sampling for Cryptococcus gattii in
geographic locations identified as “optimal” ecological
niche areas are currently underway
S. Mak, MSc thesis, 2009
Summary
• Both simple and complex statistics are useful in communicable disease epidemiology
• Are important for surveillance, outbreak response and research
• Used to: – Identify outbreaks – Examine risk factors – Persuade policy makers and the public