Weather conditions impact human health in a variety of ways . The risk and severity of health outcomes depend on exposure (atmospheric conditions), the individual or community’s sensitivity to the hazard in question, and the capacity to prepare for and manage the hazard. The major pathways through which weather and climate directly affect human health include extreme temperatures, severe weather events, and poor air quality. Climate also indirectly influences nutrition, infectious disease, and reproductive health, all of which are major areas of study. Researchers seek to identify relationships between meteorological conditions and human health, develop forecasting methods and projections to help governments and citizens better prepare for potential hazards, and understand the physical benefits (co-benefits) of mitigating climate change with renewable energy and other technologies .
The earliest studies of climate change and health focused almost exclusively on recognition of relationships between meteorological conditions and select health outcomes, and prediction of future consequences. For example, connections between extreme heat and maladies such as heat exhaustion, heat stroke, and cardiovascular disease are now well established. Recent research centers on identification of individual heat-related risk due to increased exposure (e.g., in outdoor workers), limited adaptive capacity (e.g., lack of air conditioning or other cooling), and exacerbating health problems (e.g., existing cardiovascular conditions). This illustrates an important transition from establishing a relationship to implementing related policy to reduce morbidity/mortality in high-risk groups.
Developing a deeper understanding of the mechanisms through which weather and climate change impact human health is often challenging because a single meteorological variable (such as temperature, precipitation, or humidity) can affect numerous aspects of disease ecology, requiring the development of increasingly complex models . Temperature can regulate (i) pathogen development and replication within a vector (e.g., dengue virus replication within a mosquito) or the environment (e.g., salmonella bacterium replication on food), (ii) vector population dynamics in species such as flies, and (iii) reservoir species’ behaviors and populations, like bird migration. Precipitation influences disease transmission dynamics by (i) providing water sources for vector breeding (e.g., for mosquito larvae and pupae), (ii) causing flooding that contaminates water and leads to diseases such as cholera, and (iii) triggering changes in population and behaviors of reservoir species (e.g., rodents) by flooding them out of their homes or altering their food supply. Humidity also significantly influences vector survival (e.g., in ticks) and pathogen viability in the environment (e.g., influenza).
Additionally, climate change poses long-term risks to overall wellbeing. Climate variability can adversely affect food security, especially in low- and middle-income countries. Malnutrition suppresses an individual’s ability to combat disease, thus increasing susceptibility to other adverse health outcomes. New research shows that changes in carbon dioxide concentrations driving anthropogenic climate change are reducing the quantities of important micronutrients in cereal crops, thus lessening the quality of widely-consumed staples such as wheat and rice.
Furthermore, climate change may interact with other global trends, such as increased human mobility/migration and economic insecurity, to influence demographics and health . For example, as household incomes and living conditions change, women may make different choices about their fertility. Recent research from Tanzania suggests that women who experienced crop failures were less likely to get pregnant or give birth. Weather conditions can also affect pregnancy outcomes. Extreme heat exposure during the third trimester is associated with higher rates of stillbirth, increased risk of preterm delivery, and lower birth weights.
Climate change, when combined with natural and human-induced stressors, influences health and disease in numerous ways. Image courtesy of the Centers for Disease Control and Prevention.
Greater comprehension of complex human-environment relations has yielded more advanced modeling techniques to create early-warning health systems . For instance, climate change is exacerbated by anthropogenic and naturally-occurring emissions—such as particulate matter and surface-level ozone—that contribute to poor air quality. These emissions substantially and adversely impact respiratory conditions like asthma. A large portion of the population also suffers from seasonal allergies, possibly due to increased pollen concentrations from earlier seasonal flowering in a warmer world. Indicators such as pollen counts and air quality indices are grounded in the latest science and warn the general public of unhealthy conditions. A variety of metrics, based mostly on temperature and humidity, communicate heat risk and provide recommendations to reduce exposure and negative health outcomes (e.g., avoid outdoor work or drink sufficient amounts of water). Researchers are currently focused on generating finer-scale monitoring and modeling, which can inform policymakers of new or increasing health concerns and allow them to deploy proactive interventions. Remotely-sensed data, such as the normalized difference vegetation index, provides a signifier and facilitates better monitoring of phenology changes, which indicate pollen level variations that can affect allergy sufferers.
The development of forecasting methods to enable earlier and more targeted surveillance and intervention strategies requires overcoming several challenges. Differentiating the climate’s contribution from other influences is one such challenge. Trade and travel, socioeconomic and demographic conditions, human mobility, and pathogen evolution are some of the many non-environmental factors impacting human health. Short and long-term forecasting systems must anticipate how these other components could change and interact with meteorological conditions. Therefore, models should account for these supplementary factors, in addition to uncertainty in weather and climate forecasts.
Numerical simulations can both enable the forecast of important health outcomes through climate data and provide insight into the mechanisms through which climate impacts health. However, simulation modeling is often highly dependent on parameter values, which are usually difficult to identify . Methods to estimate and evaluate unknown parameter values are needed to validate models and better understand their relative importance. The significance of seasonality in climate and health research also necessitates spatiotemporal simulations, especially for diseases. Static maps of risk are frequently of limited value because risk can vary greatly throughout a season. But climate can alter substantially in space as well; therefore, the best dynamic simulations include location-specific data. Information on neighboring areas is likewise important for modeling pathogen risk and diffusion. Unfortunately, models that include location-specific parameters and interactions between nearby locations can be complex and require considerable computing resources.
Simulations help researchers develop insights into unexpected dynamics with consequences for human health in other areas, such as toxicology. For instance, temperature can interact with toxicants to amplify disruptions to the health of organism populations in certain ecosystems, ultimately impacting the ecosystem services these populations provide . Mechanistic effect models—dynamic models based on mechanistic understanding of chemical effects on individuals—can improve chemical risk assessment and increase stakeholder and regulator utility .
Despite significant challenges, advances in weather and climate simulations, forecasting and modeling techniques, and observational tools—such as remotely-sensed data—are improving meteorologically-driven forecasts and projections of human health risks. Producing quantitative and location-specific research will be essential to helping public health professionals and policymakers make informed decisions with the greatest health benefits for their communities. This requires a robust interdisciplinary effort with contributions from a number of fields, including public health, environmental science, the social sciences, and applied mathematics. Though ambitious, such a goal is critical for assuring a healthier future for all.
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