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The good, the bad and the ugly evaluating empirical climate and health studies

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Title: The good, the bad and the ugly evaluating empirical climate and health studies


1
The good, the bad and the ugly (evaluating
empirical climate and health studies)
  • 18 July 2006
  • Sari Kovats
  • Lecturer, Public and Environmental Health
    Research Unit, LSHTM

2
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?

3
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

4
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)
5
EPIDEMIOLOGICAL STUDIES
OBSERVATIONAL (NON-EXPERIMENTAL)
6
(No Transcript)
7
Ecological studies use..
  • Average exposure for a group
  • E.g. temperature, rainfall
  • A population measure of outcome
  • Risk or Rate
  • Counts of events

8
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

9
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

10
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

11
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

12
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

13
Chance
  • Hypothesis testing
  • p-value
  • Precision of estimate
  • Confidence intervals
  • Assumes estimates/data are unbiased
  • Beware of multiple testing!

14
Confounding
Question Is alcohol consumption during pregnancy
associated with increased risk of low birthweight
Low birth weight outcome
Alcohol during pregnancy exposure
15
Time series- consider time varying confounders
High temperature exposure
Daily mortality outcome
Air pollution potential confounding factor
16
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)

17
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.
18
Strengths and weaknesses of different
observational analytic study designs
1. But high if you are not aware of, or do not
measure, confounding factors
19
Reviewing the literature
  • Develop a clear written Search strategy
  • Clear research question
  • Inclusion/exclusion criteria
  • Search gt1 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

20
Reviews- you need a search strategy
Ahern et al. 2005
21
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.
22
Abstracts
  • Identify
  • Exposure measure
  • Outcome measure
  • Study design
  • Measure of uncertainty?
  • Confounders?

23
Climate and health studies
24
Three research tasks
Empirical studies epidemiology
Risk Assessment
Scenario
Sensitivity Mechanisms Responses Causality?
Early effects? detection attribution
1970s?
future
present
25
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.

26
Exposures climate/weather parameterization
  • Long-term changes in mean temperatures, and other
    climate "norms"
  • climate change requires changes over decades or
    longer.
  • Interannual climate variability
  • including indicators of recurring climate
    phenomena El Niño years or SOI
  • Short term variability weather
  • including monthly, weekly or daily meteorological
    variables.
  • Isolated extreme events
  • simple extremes, e.g. of temperature/precipitation
    extremes.
  • complex events such as tropical cyclones, floods
    or droughts.

27
Time series analysis weekly Salmonellosis and
Temp
Sporadic cases only Outbreaks removed
Kovats et al. 2004
28
Results by age Relative risks for 5 countries,
same threshold, by age group
29
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.

30
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

31
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.

32
Systematic review ENSO and health
33
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)

34
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)
35
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/po
    pulations.
  • 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. gt 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
36
(No Transcript)
37
Summary I Get the study right
  • 1. Correct design
  • 2. As accurate a measure of exposure and outcome
    as possible
  • 3. Control confounding

38
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

39
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.

40
Thank you!
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