the good, the bad and the ugly (evaluating empirical climate and health studies) 18 july 2006 sari...
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The good, the bad and the ugly(evaluating empirical climate and health studies)
18 July 2006
Sari KovatsLecturer, Public and Environmental Health Research Unit, LSHTM
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Outline
Basic environmental epidemiology Study designs Data issues (exposure and outcome measures) Systematic reviews
Discuss abstracts Climate and health studies
Time series (again) Inter-annual variability Trends: early effects of climate change?
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Environmental epidemiology
Disease driven approach Identification of disease endpoints, followed by the
examination of potential hazards in effort to establish causation
Exposure-driven approach Identifying potential hazards and then examining their
effects on human health
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Exposures and outcomes
In an epidemiological study there are:
(a) the outcome of interest
(b) the primary exposure (or risk factor) of interest
(c) other exposures that may influence the outcome (potential confounders)
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EPIDEMIOLOGICAL STUDIES
OBSERVATIONAL (NON-EXPERIMENTAL)INTERVENTION (EXPERIMENTAL)
We observe only We allocate exposure
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EPIDEMIOLOGICAL STUDIES
OBSERVATIONAL (NON-EXPERIMENTAL)INTERVENTION (EXPERIMENTAL)
DATA FROM GROUPS
DATA FROM INDIVIDUALS
DATA FROM GROUPS
DATA FROM INDIVIDUALS
DESCRIPTIVE(a)
ANALYTIC ANALYTICDESCRIPTIVE
CLINICAL TRIAL,INDIVIDUAL FIELD
TRIAL (g)
ECOLOGICALSTUDY
(b)
CROSS-SECTIONALSTUDY
(c)
COHORTSTUDY
(d)
CASE-CONTROLSTUDY
(e)
COMMUNITY TRIAL
(f)
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Ecological studies use..
Average exposure for a group E.g. temperature, rainfall
A population measure of outcome –
Risk or Rate Counts of events
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Ecological studies
Strengths Quick and relatively inexpensive Simple to conduct Availability of data from surveillance programs and disease
registries
Limitations Difficulties in linking exposure with disease Limitations in controlling for potential confounding factors
time series avoids some confounding issues…. “Ecological fallacy” – making a causal inference about an
individual phenomenon or process on the basis of group observations
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Situations where group level variables may be better
Exposures without much within group variability (salt consumption in U.S.)
Exposures which can only be measured at population level Herd immunity in studying infectious disease
(vaccination levels may be more informative than individual behavior)
Social capital Climate
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Cross-sectional studies
also called survey or prevalence study measures exposure and outcome at the same point in
time involves disease prevalence usually involves random sampling and questionnaire
measurement cannot distinguish whether hypothesized cause preceded the
outcome
Spatial/geographical studies: links environmental data with survey data
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Case control studies
Example. Chicago heat wave 1999 Naughton et al. Cases: 63 deaths from heat stroke during heat wave Control – 77 alive controls, matched on age and
neighbourhood. Cases - Range of social, environmental risk factors for heat wave
deaths “Working air conditioner at home” Odd Ratio 0.2 (95% CI
1.0, 0.7) Must consider selection of controls Cannot calculate rates or attributable risks
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Bias
Selection bias how were subjects selected for investigation how representative were they of the target population with regard to
the study question?
Information bias (recall bias) what was the response rate, and might responders and non-
responders have differed in important ways? how accurately were exposure and outcome variables measured? Random vs. systematic errors – have different implications for final
estimate
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Chance
Hypothesis testing p-value
Precision of estimate Confidence intervals
Assumes estimates/data are unbiased Beware of multiple testing!
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Confounding
Question: Is alcohol consumption during pregnancy associated with increased risk of low birthweight
Alcohol during pregnancyexposure
Low birth weightoutcome
Smoking during pregnancypotential confounding factor
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Time series- consider time varying confounders
High temperatureexposure
Daily mortalityoutcome
Air pollutionpotential confounding factor
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Epidemiological data
Routine sources of health data Vital Registration (births, deaths) Hospital statistics (admissions, clinic attendance) Primary care Laboratory data (notifiable diseases)
Health Surveys Epidemiological Studies (cohort or longitudinal studies,
cross-sectional surveys) Demographic and Health Surveys (low and middle
income countries)
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Ecological
Cross-
sectional
Case-control
Cohort
Investigation of rare disease
++++
-
+++++
-
Investigation of rare exposures
++
-
-
+++++
Examining multiple outcomes
+
++
-
+++++
Studying multiple exposures
++
++
++++
+++
Measurement of time relationship between exposure and outcome
+
-
+
+++++
Direct measurement of incidence
-
-
+ 1
+++++
Investigation of long latent periods
-
-
+++
+++ 2
Applications of different observational and analytical study designs
1 Unless the sampling fraction is known for both cases and controls; i.e. unless the proportion of cases and
proportion of controls sampled from the population is known.
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Ecological
Cross sectional
Case control
Cohort
Probability of:
selection bias
information bias
loss to follow-up
confounding
NA
NA
NA
high
medium
high
NA
medium
high
high
NA
medium
low
low
high
low
Time required
low
medium
medium
high
Cost
low
medium
medium
high
Strengths and weaknesses of different observational analytic study designs
1. But high if you are not aware of, or do not measure, confounding factors
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Reviewing the literature
Develop a clear written Search strategy Clear research question
Inclusion/exclusion criteria Search >1 database, plus hand searching, snowballing..
Some assessment of quality of studies Limit to peer review published articles only.
Beware publication bias Language bias Climate change bias! – editors like novel or hot topics
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Reviews- you need a “search strategy”
Ahern et al. 2005
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Quality control: flooding and health studies Clearly stated hypothesis Individuals included in the study and how they were selected (i.e. using
some form of randomisation or probability sampling procedure) Sample to include those who were affected by the flood event, and those
who were not. The latter are often referred to as the ‘control’ or ‘comparison’ group
Data collection in both the pre- and post-flood period. Prospective data collection is given higher weighting than retrospective data collection, as the latter is particularly susceptible to recall bias
Results should include p-values or confidence intervals, and limitations of the study should also be highlighted
Clinical (e.g. mental health outcomes) or laboratory (e.g. leptospirosis) diagnosis is given greater credence than self-reported diagnosis.
Ahern et al. 2006 Flood Hazards and Health. EarthScan Book.
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Abstracts
Identify Exposure measure Outcome measure Study design Measure of uncertainty? Confounders?
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Climate and health studies
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1970s=? futurepresent
SensitivityMechanismsResponsesCausality?
Early effects?detectionattribution
Three research tasks
Empirical studies[epidemiology]
Scenario
Risk Assessment
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IPCC: different types of evidence for health effects
Health impacts of individual extreme events (heat waves, floods, storms, droughts);
Spatial studies, where climate is an explanatory variable in the distribution of the disease or the disease vector
Temporal studies (time series), inter-annual climate variability, short term (daily, weekly) changes (weather) longer term (decadal) changes in the context of detecting
early effects of climate change. Experimental laboratory and field studies of vector,
pathogen, or plant (allergenic) biology.
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Exposures: climate/weather parameterization
Long-term changes in mean temperatures, and other climate "norms" o climate change requires changes over decades or longer.
Interannual climate variability o including indicators of recurring climate phenomena – [El Niño years or SOI]
Short term variability [weather] o including monthly, weekly or daily meteorological variables.
Isolated extreme eventso simple extremes, e.g. of temperature/precipitation extremes.o complex events such as tropical cyclones, floods or droughts.
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Time series analysis: weekly Salmonellosis and Temp
0500
1000
1500
Weekly
cases
0 5 10 15 20Temperature
City/Country ThresholdoC
% 95% CI
Adelaide M No 4.9% 3.4, 6.4
Perth M No 4.1% 3.1, 5.2
Brisbane M No 11.0% 7.7, 11.2
Melbourne M No 5.1% 3.8, 6.5
Sydney M No 5.6% 4.3, 7.0
Canada W
Poland M 6 (. , 7) 8.7% 4.7, 12.9
Scotland W 3 (., 12) 5.0% 2.2, 7.9
Denmark W 15 (., .) 0.3% - 1.1, 1.8
England & Wales W 5 (5, 6) 12.5% 11.6, 13.4
Estonia W 13 (3, 14) 9.2% - 0.9, 20.2
Netherlands W 7 (7, 8) 8.8% 8.0, 9.5
Czech Republic W -2 (-6, -1) 9.2% 7.8, 10.7
Switzerland W 3 (., 3) 9.1% 7.9, 10.4
Slovak Republic 2W 6 (., .) 2.5% - 2.6, 7.8
Spain W 6 (., 8) 4.9% 3.4, 6.4
Kovats et al. 2004
Sporadic cases onlyOutbreaks removed
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0.95
1
1.05
1.1
1.15
1.2
(0-14)
(15-64)
(65+) (0-14)
(15-64)
(65+) (0-14)
(15-64)
(65+) (0-14)
(15-64)
(65+) (0-14)
(15-64)
(65+)
SC DK EW NL CH
Results by age: Relative risks for 5 countries, same threshold, by age group
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Time lags/time windows
Acute events Cause before effect (temporality) Use literature to hypothesise the time lags (days)
Need to address incubation period for infectious diseases 1-2 days salmonellosis, 7-14 days typhoid fever Delays in reporting process
Critical time windows Aetiological relevant exposure windows
E.g. childhood exposures to UV, in utero exposures Need to address latency periods (?years) between exposure and
outcome.
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ENSO and health Large scale climate phenomenon Irregular occurrence Climate variability can be important driver of year to year variation in
disease. ?driven by precipitation
Insight into effects not evident at local scales rainfall, predator balance (Venezuela)
Applications Epidemic prediction using seasonal forecasts Effects of increased frequency of ENSO events under climate
change But cannot directly assess effects of progressive warming from
direct extrapolation of ENSO-health relationships
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Systematic review – ENSO and health Criteria for inclusion.
Published in peer reviewed journal Original research article using epidemiological data. Quantified association with an ENSO parameter (e.g. El Niño
year, SST, SOI or other index). The outcome was an infectious disease in humans. The time series included more than one El Niño event.
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District or country Outcome Time period ENSO parameter Brazil Annual incidence 1956–1998 SOI, El Niño year Colombia, Antioquia Monthly cases 1980–1997 El Niño year Colombia Annual cases 1960–1992 El Niño year / SST Colombia Annual incidence 1959–1998 SOI, El Niño year Ecuador Annual incidence 1956–1998 SOI, El Niño year French Guiana Annual incidence 1971–1998 SOI, El Niño year Guyana Annual incidence 1956–1998 SOI India + Pakistan (Punjab) 1867–1943 El Niño year / SST Kenya, Kericho in western highlands
Monthly cases 1966–1998 MENSOI
Pakistan (northern region) Annual incidence 1970–1993 SST Peru Annual incidence 1972–1999 SOI Sri Lanka, South west region
Epidemic years 1870–1945 El Niño year / SST
Surinam Annual incidence 1956–1998 SOI Venezuela Annual incidence 1956–1998 SOI Venezuela, coastal region Annual deaths 1910–35 El Niño year / SST Venezuela Annual cases 1975–90 El Niño year / SST
Systematic review – ENSO and health
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Evaluating ENSO-health studies
Need to identify correct climate “driver” Biological mechanisms Alternative explanations,
e.g. cyclical changes in immunity Hay et al. Inter-epidemic periods in mosquito-borne diseases Dengue – new serotypes on population
Limited data series - need more than 1 event..
Most appropriate geographical aggregation Disease data is of uncertain quality (and may not be disease-
specific)
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Tick-borne Encephalitis, Sweden: 1990s vs 1980s:
winter warming trend
Early1980s
Mid-1990s
White dots indicate locations where ticks were reported. Black line indicates study region.(Lindgren et al., 2000)
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Evaluating early effects: Criteria.. What constitutes evidence of early effects?
To detect changes in distribution or phenology/seasonality, sample sizes should be maximised by studying multiple species/diseases/populations.
To detect polewards or altitudinal shifts in vector or disease distributions, studies should extend across the full range (Parmesan 1996), or at least the extremes of the range. (Parmesan et al. 2000), so as to exclude simple expansions or contractions.
Given the natural variability in both climate and biological responses, long data series are needed (i.e. > over 20 years).
Variability in the climate series (e.g. year to year) should correspond to variability in the health time series.
Analyses should take into account, as far as possible, other changes that have occurred over the same time period which could plausibly account for any observed association with climate.
Kovats et al. 2001
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Surveys up to 1940
Surveys up to 2000Surveys up to 1980
Surveys up to 1960
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Summary I: Get the study right
1. Correct design
2. As accurate a measure of exposure and outcome as possible
3. Control confounding
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Summary II: Evaluating
Reviews must be systematic and thorough Epidemiological literature must be evaluated Climate and health studies should have..
clear hypotheses plausible biological mechanisms reported validity and precision
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Summary III: Criteria
Good studies……………. measure and control confounders; describe the geographical area from which the health data are
derived; use appropriate observed meteorological data for population of
interest (the use of reanalysis data may give spurious results for studies of local effects);
have plausible biological explanation for association between weather parameters and disease outcome;
remove any trend and seasonal patterns when using time-series data prior to assessing relationships;
report associations both with and without adjustments for spatial or temporal autocorrelation.
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Thank you!