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

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?

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

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)

EPIDEMIOLOGICAL STUDIES

OBSERVATIONAL (NON-EXPERIMENTAL)INTERVENTION (EXPERIMENTAL)

We observe only We allocate exposure

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)

Ecological studies use..

Average exposure for a group E.g. temperature, rainfall

A population measure of outcome –

Risk or Rate Counts of events

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

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

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

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

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

Chance

Hypothesis testing p-value

Precision of estimate Confidence intervals

Assumes estimates/data are unbiased Beware of multiple testing!

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

Time series- consider time varying confounders

High temperatureexposure

Daily mortalityoutcome

Air pollutionpotential confounding factor

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)

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.

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

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

Reviews- you need a “search strategy”

Ahern et al. 2005

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.

Abstracts

Identify Exposure measure Outcome measure Study design Measure of uncertainty? Confounders?

Climate and health studies

1970s=? futurepresent

SensitivityMechanismsResponsesCausality?

Early effects?detectionattribution

Three research tasks

Empirical studies[epidemiology]

Scenario

Risk Assessment

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.

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.

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

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

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.

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

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.

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

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)

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)

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

Surveys up to 1940

Surveys up to 2000Surveys up to 1980

Surveys up to 1960

Summary I: Get the study right

1. Correct design

2. As accurate a measure of exposure and outcome as possible

3. Control confounding

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

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.

Thank you!