Climate change and health modeling: Horses for courses

Abstract

Mathematical and statistical models are needed to understand the extent to which weather, climate variability, and climate change are affecting current and may affect future health burdens in the context of other risk factors and a range of possible development pathways, and the temporal and spatial patterns of any changes. Such understanding is needed to guide the design and the implementation of adaptation and mitigation measures. Because each model projection captures only a narrow range of possible futures, and because models serve different purposes, multiple models are needed for each health outcome ('horses for courses'). Multiple modeling results can be used to bracket the ranges of when, where, and with what intensity negative health consequences could arise. This commentary explores some climate change and health modeling issues, particularly modeling exposure-response relationships, developing early warning systems, projecting health risks over coming decades, and modeling to inform decision-making. Research needs are also suggested.

Full text

METHODS FORUM
Climate change and health modeling: horses for courses
Kristie L. Ebi* and Joacim Rocklo¨v
Epidemiology and Global Health, Department of Public Health and Clinical Medicine, Umea
˚Centre for
Global Health Research, Umea
˚University, Umea
˚, Sweden
Mathematical and statistical models are needed to understand the extent to which weather, climate variability,
and climate change are affecting current and may affect future health burdens in the context of other risk
factors and a range of possible development pathways, and the temporal and spatial patterns of any changes.
Such understanding is needed to guide the design and the implementation of adaptation and mitigation
measures. Because each model projection captures only a narrow range of possible futures, and because
models serve different purposes, multiple models are needed for each health outcome (‘horses for courses’).
Multiple modeling results can be used to bracket the ranges of when, where, and with what intensity negative
health consequences could arise. This commentary explores some climate change and health modeling
issues, particularly modeling exposure-response relationships, developing early warning systems, projecting
health risks over coming decades, and modeling to inform decision-making. Research needs are also
suggested.
Keywords: climate change;climate variability;modeling;health impacts;research needs
Responsible Editor: Nawi Ng, Umea˚ University, Sweden.
*Correspondence to: Kristie L. Ebi, Epidemiology and Global Health, Department of Public Health and
Clinical Medicine, Umea˚ Centre for Global Health Research, Umea˚ University, SE-901 85 Umea˚, Sweden,
Email: krisebi@essllc.org
Received: 23 February 2014; Revised: 28 April 2014; Accepted: 29 April 2014; Published: 23 May 2014
The current and projected human health conse-
quences of climate change are diverse and wide-
ranging, potentially altering the burden of any
health outcome sensitive to weather or climate. Climate
variability and change can affect morbidity and mortality
from extreme weather and climate events, and from
changes in air quality arising from changing concentra-
tions of ozone, particulate matter, or aeroallergens (1).
Altering weather patterns and sea level rise also may
facilitate changes in the geographic range, seasonality, and
incidence of selected infectious diseases in some regions,
such as malaria moving into highland areas in parts of
sub-Saharan Africa (13). Changes in water availability
and agricultural productivity could affect undernutrition,
particularly in parts of Asia and Africa (4). The pathways
between climate change and these health outcomes are
often complex and indirect.
Excluding the direct health burdens from extreme
weather and climate events, the standard for evidence-
based exposure-response relationships for climate-sensitive
health outcomes differs from that for many environmental
and occupational exposures. No health care use or death
certificate will have climate change as an underlying
or contributing cause; instead, the records focus on the
physiological mechanism(s) associated with the event.
Further, these health outcomes generally have multiple
and often interacting drivers of their geographic range,
incidence, and seasonality; factors that are infrequently
recorded on patient care records and death certificates.
These records and death certificates are not designed
to consider that climate change may have contributed to
a case of (or death from) diarrheal disease by increasing
the rate of pathogen replication in warmer ambient tem-
peratures or by facilitating the distribution of pathogens
by increased heavy precipitation events.
Given the many and complex linkages between climate
processes and a health outcome, understanding the extent
to which weather, climate variability, and climate change
are affecting and may affect future health burdens in the
context of other risk factors, and the temporal and spatial
patterns of any changes, is challenging if not impossible
without developing mathematical and statistical models.
Ideally, such models are based on the association between
the exposure and outcome, taking into consideration the
relevant processes and drivers, and possible sources of
confounding and bias. The question is not whether to
model, but whether there are sufficient data over long-
enough time periods and robust enough understanding of
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the processes relating the climate factors to health out-
comes to develop useful models and, if so, how to model. If
appropriately designed and communicated effectively,
policy- and decision-makers can use health models inte-
grating micro- to macro-level exposures and processes
that influence the occurrence of a health outcome to se-
lect a basket of policies and measures to efficiently and
effectively manage health risks over shorter and longer
temporal scales.
Scientific curiosity alone does not drive modeling the
health risks of current and projected climate change. Models
are designed to answer specific policy-relevant questions.
Two principal aims are to guide adaptation measures to
increase resilience, and to guide mitigation policies to
reduce greenhouse gas emissions, thereby reducing health
risks past mid-century. Common purposes for health
models are to understand exposure-response relationships
(based on biological or statistical relationships), estimate
the relative contributions of different risk factors, map the
locations of populations vulnerable to particular health
outcomes or environmental conditions, develop early
warning systems, and project longer-term health risks
under different climate and socioeconomic scenarios. The
data needs, the spatial and temporal scales, and the model
structure and robustness vary across models, with no one
model able or robust enough to answer all questions at
all scales.
Horses for courses is an idiom that captures that
multiple models are needed for each health outcome of
concern because different models serve different uses, with
each model better than other models at addressing a
particular issue. Models developed for one purpose may
not be appropriate for other purposes. In fact, it is highly
unlikely that a model developed as the basis of an early
warning system, which thus contains sufficient detail and
contextual information to be able to forecast where and
when health risks could increase under particular environ-
mental conditions in a specific location, would also be
appropriate for projecting how health risks could evolve
over this century under different climate and socioeco-
nomic scenarios at a range of spatial and temporal scales.
Considerations in model development include quanti-
fication of the climatehealth associations and the factors
that affect those associations; specification of model(s)
appropriate to incorporate climate variability and change,
adaptation, and mitigation policies, including considera-
tion of temporal and spatial scale issues; incorporation
of thresholds; incorporation of pathways of public health
development; and quantification of uncertainties (5). The
description of a model should include the conceptual
framework underlying its structure, key assumptions, and
its temporal and spatial scale. The usefulness of a model
and its output of current or future climate-sensitive health
burdens depend on not only the robustness of the model,
but also the extent to which the model provides results that
address the needs of stakeholders, at the appropriate scales
of interest.
This commentary explores some issues with modeling
the health risks of climate change, particularly modeling
exposure-response relationships, developing early warn-
ing systems, projecting health risks over coming decades,
and modeling to inform decision-making. We conclude
with some suggested research needs.
Modeling exposure-response relationships
Public health and health-care organizations demand,
appropriately so, quantitative evidence of the associations
between risk factors and health outcomes, and of the
effectiveness, including costs and benefits, of interventions
before modifying current or implementing new measures
(e.g. early warning systems) to reduce exposures and
vulnerabilities. Long-term data sets are required to esti-
mate the contributions of various factors to health
burdens. Even when such data sets are available, the
complexities of interactions in the chain of causation for a
climate-sensitive health outcome mean that modeling is
often the best approach for understanding how and
to what extent weather and climate patterns contribute
to health outcomes, in the context of multiple drivers, and
for furthering understanding of possible interventions to
reduce health risks. For example, increasing summertime
ambient temperatures increases the risks of heat-related
morbidity and mortality. The urban surrounding, includ-
ing built infrastructure (e.g. green roofs, location of
cooling centers), the presence of trees, and other factors
can modify hot temperatures, making some locations
within a community warmer and others cooler. The extent
to which individuals are sensitive to hot temperatures
depends on their level of fitness, presence of certain
chronic diseases, use of drugs that affect thermal regula-
tion, clothing choices, and other factors. Understanding
the relative importance of different factors through
modeling can be helpful for targeting interventions to
reduce morbidity and mortality.
In biological and process-based models, clearly de-
scribed causal interrelationships are used to simulate
disease dynamics. Statistical models often have different
requirements. Data-driven models can be developed in
cases where the underlying disease processes are not well
understood or well quantified, and where there is, for
example, insufficient information to develop a causal
pathway model. In such cases, a simple model would
always be preferable to a more complex model given
similar predictive ability. It is always important for models
to be validated using data not used in model fitting to
avoid over-fitting and to increase confidence in the
predictive accuracy in new situations.
Kristie L. Ebi and Joacim Rocklo
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Mapping vulnerable regions and using
exposure-response relationships to develop
early warning systems
Mapping can be very helpful for local and national
decision-makers to enhance current health protection,
and can be used to support vector and disease surveillance
and early warning systems on the timing and scale of
vector control operations and public health interventions,
more effectively deploying scarce human and financial
resources. Early warning systems are based on forecasts
of how the numbers of cases vary by levels of environ-
mental variables, balancing the need for the time needed
to prepare for an outbreak with the skill of the forecast
over different temporal scales.
For example, the increasing worldwide burden of
dengue fever has led to interest in mapping where dengue
is currently (or may soon be) transmitted. The distribution
of dengue depends not just on the temperature and
precipitation requirements and constraints of the vector
and pathogen, but also on vector control and surveillance
programs (and their effectiveness), awareness and effec-
tiveness of health-care organizations in diagnosis and
treatment, how travel and trade affect vector and pathogen
distribution, and urbanization patterns. Dengue early
warning systems are important for managing outbreaks,
particularly with the lack of a vaccine or treatment options.
For example, weekly mean temperature and cumulative
rainfall in combination with disease surveillance can be
used to forecast dengue epidemics 16 weeks in advance in
Singapore (6).
One of the challenges with the models underlying early
warning systems is that they assume that historic distribu-
tion patterns and relationships will remain constant in the
future. However, this may not always be the case. For
example, increasing ambient temperatures will lead to
acclimatization that could alter the relationships between
heat waves and morbidity and mortality. For infectious
diseases, changing disease transmission dynamics and con-
trol policies means that models are likely to need adjust-
ment to maintain forecast precision as weather patterns
shift over time.
Applying models in other regions or at other
scales
The use of a model needs to be consistent with its
development. For example, a model validated in one com-
munity over a particular time period to support an early
warning system may not provide valid predictions in other
communities or at other temporal scales. Although an
early warning system can provide guidance on key factors
and approaches for developing such a system elsewhere,
analyses will be needed of how local transmission patterns,
key contextual factors, and other variables could alter the
early warnings or responses.
There is often a trade-off between increasing the good-
ness of fit of a model versus improving its predictive ability.
For example, risk maps of malaria and dengue using
spatial information of reported disease frequency are
sometimes validated locally by matching to a nearby
nontransmission zone, including matching on local weath-
er patterns. Such models are trained to distinguish local
differences in the predictor variables on the disease out-
come but are not trained to discriminate global influences
that may alter disease risks, such as longer-term changing
weather patterns with climate change (7). Therefore,
such models should not be used for global projections
of risk or incidence under climate change. Also, a model
that effectively discriminates on local features may be
ineffective or misleading if applied at the global level.
Similarly, model validation to one location provides no
guarantee that using the model for another location would
be appropriate because contextual factors can indirectly
influence model parameters. For global (local) predictions
and projections, models validated with global (local) data
are the most accurate.
Projecting possible future health risks
While increasingly detailed understanding of the determi-
nants of a climate-sensitive health outcome on short time
scales can enhance the effectiveness of public health inter-
ventions, this level of detail may not be needed or useful
to provide realistic projections of disease burdens in mid-
century and beyond under different possible futures.
Projecting the extent to which alterations in weather
patterns may affect future health burdens requires moving
beyond models only based on exposure-response relation-
ships and projected temperature/precipitation change to
models that incorporate a range of plausible (and relevant)
environmental and socioeconomic futures. For example,
variables that can influence the burden and pattern of
vector-borne diseases include weather patterns, land use,
demographic growth, and factors associated with socio-
economic development, such as the status of the health
system, the availability of human and financial resources,
diagnostic and treatment technologies, and others.
Models can be used to identify which of the many
complex interrelationships and interdependencies that
determine a health outcome are the most important to
describe its current geographic range, seasonality, and
incidence, and to project how these might change when
one or more parameters change. Not all drivers of a
climate-sensitive health outcome are of equal importance
for understanding the role(s) of climate variability and
change. Models can provide insights into which variables
and interactions are critical.
Lyme disease is an example of a disease where increas-
ing model complexity is not always needed. Lyme disease
Climate change and health modeling
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has a multi-year life cycle that includes four life stages: egg,
six-legged larva, eight-legged nymph, and adult. Blood
meals are required during the last three stages for tick
survival and reproduction. Human pathogens acquired
during feeding can be transmitted at the next feeding stage.
Ogden et al. (8) reviewed knowledge of the drivers for
changes in the geographic range of ticks and tick-borne
pathogens via effects on their basic reproduction number
R
0
, and the mechanisms of dispersal that allow ticks and
tick-borne pathogens to invade suitable environments.
R
0
identifies when the Lyme pathogen, Borelia burgdoferi,
can spread when introduced into a fully susceptible
population. Therefore, R
0
is an index of species fitness in
current or possible future locations. Many observed
patterns are consistent with expectations from theoretical
studies or studies of other species. Key factors determining
the expansion and invasion of Ixodes scapularis ticks and
B. burgdoferi are changes in climate, habitat, and host.
Dunn et al. (9) developed a mechanistic model of whether
B. burgdoferi can spread when introduced into a fully
susceptible population. Nineteen parameters of biological
relevance were included, taking into account the transmis-
sion efficiency from the vertebrate host as a function of the
days since infection. Three factors were the most influen-
tial in terms of their contribution to the observed variation
in R
0
: transmission efficiency from the vertebrate hosts to
I. scapularis ticks (which depends on the innate suscept-
ibility of the host community to infection), tick survival
rate from fed larva to feeding nymph (largely determined
by ambient temperature), and the fraction of nymphs
finding a competent host (largely determined by the
composition of the host community and the relative
densities of reservoir host species). Given the widespread
distribution of possible hosts, modeling how climate
change could alter the geographic range of Lyme disease
can therefore be simplified to focus on temperature, as the
susceptibility and composition of the host community
may not be constraints when the vector and pathogen
expand into new regions.
Modeling to inform policy- and decision-making
Insights gained from modeling how climate change could
affect future health burdens at local to regional scales,
and across near-term to longer time slices, are important
for prioritizing adaptation and mitigation strategies to
increase the resilience of future societies, particularly for
processes with longer-term planning horizons. For ex-
ample, sea level rise, storm surges, and increasingly heavy
precipitation may mean that hospitals need to be moved
from flood-prone areas. Models can indicate the timing,
location, and level of possible risks, and likely safe
distances from coastlines, major rivers, and other sources
of flooding risk. Modeling also can quantify the extent
to which urban design could increase active transport
possibilities, reduce flooding risks, and result in other
co-benefits.
Model development should consider not only what
scientists know about weather/climate health relation-
ships, but also what stakeholders need to know for effec-
tive decision-making (10). The purpose of projecting
health burdens is to gain insights into general trends
and patterns, including key interactions and dynamics,
under different scenarios of future climate and develop-
ment pathways. Doing so provides decision-makers with
a broad-brush vision of how health burdens could change
to inform longer-term planning. Because the future is un-
known, one should view projections not as precise esti-
mates of what will happen, but as more order-of-magnitude
estimates of what could happen if the underlying assump-
tions of climate and development are met.
Policymakers can use model results to identify mitiga-
tion and adaptation targets, and approaches to meeting
those targets that promote human health and well-being.
To be most effective, modeling should be an iterative
process involving scientists and policymakers to ensure
that results contribute to the body of scientific knowledge
and address the needs of decision-makers. National and
international assessments often facilitate this upstream/
downstream communication by identifying not only key
findings from published literature, but also knowledge
gaps that, if filled, would enhance the policy relevance of
research.
Because each model projection only captures a narrow
range of possible futures, a necessary investment for
managing the health risks of weather, climate variability,
and climate change is to have multiple models of when,
where, and with what intensity negative health conse-
quences could arise over shorter and longer time scales,
to bracket the ranges of future health burdens to which
regions and nations will need to adapt and mitigate. Any
one model may miss the key dynamics affecting future
uncertainties. Similar motivations have led to the devel-
opment of multiple models in other sectors as well as
model intercomparison projects (e.g. the World Climate
Research Program for earth system models (http://www.
wcrp-climate.org/) or the Agricultural Model Intercom-
parison and Improvement Project (AgMIP; www.agmip.
org) that includes activities for model intercomparisons
and improvement).
Research needs
Developing conceptual frameworks and modeling of the
interrelationship among the determinants of a health
outcome can identify research needs by highlighting where
additional information could significantly improve under-
standing that can then, in turn, improve model validity
and usefulness. Models, from exposure-response relation-
ships to projections of the health risks of climate change,
are needed to explore the range of potential impacts of
Kristie L. Ebi and Joacim Rocklo
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weather and climate in the context of other drivers of
population health. Modeling should be a core activity in
research centers and public health organizations to facil-
itate understanding and risk management of the health
risks of climate change.
While considerable progress has been made in under-
standing the risks of extreme ambient temperatures and
how changing weather patterns could affect the geo-
graphic range, seasonality, and incidence of some infec-
tious diseases, much more needs to be learned to develop
exposure-response relationships to underpin effective
public health interventions, including early warning and
response systems. Low funding levels means efforts are
limited for developing models that explore how drivers of
climate-sensitive health outcomes interact under a range
of possible future climatic and socioeconomic conditions
to further planning for managing future health risks.
Models are needed to explore not just how climate change
could impact individual health outcomes, but also possible
impacts across interacting health outcomes, such as
diarrheal disease, malaria, and undernutrition.
Developing modeling protocols and guidelines, includ-
ing the types of models appropriate for particular pur-
poses, considerations of geographic and temporal scales,
approaches to validation, and handling of uncertainties,
would enhance the usefulness and relevance of models.
Doing so would increase comparability, facilitating inter-
model comparisons. Malaria is one of the few health out-
comes modeled by more than one research group, which
provided a recent opportunity for an intermodel compar-
ison (2). This paper clearly shows that major contributors
to the variability in the projected risks include conceptual
differences in the models, model parameterization, whether
the model was developed for early warning or to project
risks in a changing climate, and local to global validation
schemes. Developing more consistent approaches to
modeling malaria risk would increase confidence in the
robustness of results found by multiple research groups,
and likely decrease the inconsistencies across global im-
pact projections that at least partially arise from using
models developed for different purposes. Further, models
projecting risks should better incorporate nonclimatic
drivers of future vulnerability, for example, based on the
Shared Socioeconomic Pathways (11).
Model results should be communicated across local-
to-national scales, to build capacity of relevant stake-
holders at all levels, including research, public health and
health-care managers, user communities, and the public.
Collaboration across these communities is needed to
iterate to effective solutions to manage current disease
burdens from climate change, taking the local context
into consideration, and to continue iterating as further
climate change requires adjustments to models and public
health and health-care programs to prepare for and cope
with projected risks.
Conflict of interest and funding
The authors have not received any funding or benefits from
industry or elsewhere to conduct this study.
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