Designing and conducting climate change and health IR studies

Quantitative methods involve the collection and analysis of numerical data to identify patterns, test hypotheses, and establish generalizable findings. In CCH projects, this may include statistical analyses of health outcomes related to temperature variations, air quality indices or the prevalence of climate-sensitive diseases. For instance, time-series analyses can elucidate correlations between heatwaves and increased hospital admissions for heat-related illnesses.

Why it matters in CCH IR:

• Allows researchers to measure the scope and severity of CCH issues.
• Identifies trends over time, such as the long-term health effects of rising temperatures.
• Provides evidence for decision-making, helping policy-makers allocate resources efficiently and appropriately.

Common quantitative approaches in CCH IR:

• Epidemiological studies – analyse disease trends linked to climate change (e.g. malaria incidence and temperature shifts).
• Time-series analysis – assess long-term trends (e.g. spikes in respiratory illnesses during wildfire seasons).
• Surveys and structured questionnaires – collect data on health-related behaviours, service access and adaptation strategies.

Qualitative methods such as interviews, focus groups and ethnographic observations provide nuanced insights into how communities perceive climate risks, adapt to environmental changes and access relevant health services. For example, semi-structured interviews with residents in flood-prone areas can reveal barriers to evacuation and inform the development of more effective communication strategies.

Why it matters in CCH IR:

• Explains why interventions succeed or fail in different settings.
• Captures community perspectives on climate risks and adaptation challenges.
• Helps tailor policies and interventions to local realities.

Common qualitative approaches in CCH IR:

• In-depth interviews – for example engaging health workers, policy-makers and affected individuals to understand their perspectives.
• Focus groups – gather shared insights on CCH challenges and adaptation strategies.
• Ethnographic observation – study health care settings to assess how frontline workers manage CCH risks.

Mixed methods research integrates numerical data (quantitative) and contextual insights (qualitative) to provide a comprehensive understanding of CCH-related issues.

Why it matters in CCH IR:

• Provides a comprehensive understanding of both measurable trends and the contextual factors that influence real-world outcomes.
• Helps explain unexpected results (e.g. why a policy/intervention succeeds in one area but fails elsewhere).
• Identifies barriers to implementation that quantitative data alone might overlook.
• Ensures policy and intervention recommendations are grounded in both statistical evidence and lived experiences.

This approach is particularly helpful for studies that wish to explore not just whether an IR intervention succeeded or failed, but also how or why.¹⁸ Table 4 outlines common mixed methods study designs used in CCH IR.

A mixed methods approach is a valuable tool for informing evidence-based decision-making and improving intervention effectiveness. However, researchers should also be aware of the challenges this method presents:

• Time- and resource-intensive – collecting, analysing and integrating diverse datasets requires additional effort and expertise.
• Potential inconsistencies – quantitative and qualitative findings may not always align, requiring deeper interpretation and triangulation.

While single-event studies capture snapshots of immediate health crises, they do not track long-term effects such as shifting disease burdens, cumulative exposure risks or the effectiveness of adaptation strategies.

For example:

• Rising temperatures and air pollution contribute to the gradual increase in respiratory diseases over decades.
• Vector-borne diseases such as malaria and dengue are slowly spreading to new regions as global climate patterns shift.
• Long-term exposure to heat stress, food insecurity and displacement exacerbates mental health challenges over time.

Longitudinal population studies track the health of large groups over time, providing insights into how environmental, socioeconomic and genetic factors influence health throughout the life course.²⁹ They are crucial for understanding how climate change impacts health outcomes over time – revealing trends and delayed effects that short-term research might miss. This approach is vital for identifying gradual health impacts and assessing the effectiveness of interventions.

Longitudinal studies are an invaluable research tool, but they can be challenging and resource intensive.⁵³ Specifically:

• High costs: Maintaining long-term research platforms is expensive, often requiring substantial financial resources for data collection, participant retention and infrastructure upkeep.
• Participant attrition: Over time, participants may drop out due to various reasons, leading to potential biases and reduced data reliability.
• Research fatigue: Continuous participation can lead to fatigue among subjects, affecting their engagement and the quality of data collected (see Box: Longitudinal research and the challenge of research fatigue).

Nonetheless, the insights gained from longitudinal studies are invaluable. They provide critical evidence to inform public health interventions and policy decisions aimed at mitigating the adverse health effects of climate change.

Effective CCH implementation research requires reliable data, often from multiple sources. These data help researchers track environmental changes, assess health impacts and develop early warning systems (EWS) for climate-sensitive diseases. CCH monitoring draws from a mix of meteorological, environmental and health datasets, often integrating information across sectors.

This section highlights the different types of data available and their role in studying climate impacts on health. Table 6 provides a list of relevant sources for each data type.

Meteorological data plays a crucial role in CCH monitoring, providing insights into how environmental changes impact public health.

• Heatwaves and temperature extremes can lead to severe heat-related illnesses,^41,56 making temperature data essential for EWS and adaptation strategies.
• Air pollution trends, influenced by climate shifts, affect respiratory health, with long-term data helping to track exposure risks.⁵⁷
• Vector-borne disease spread is closely linked to climate conditions, as rising temperatures and changing rainfall patterns alter mosquito and tick habitats,⁵⁸ increasing/extending disease transmission. See Box: Applying IR to climate-informed disease surveillance.
• Extreme weather events such as hurricanes, droughts and floods pose both immediate and long-term health risks, from injuries and waterborne diseases to food insecurity and displacement.⁵⁹

Understanding these patterns through sustained meteorological monitoring allows for better preparedness and targeted public health interventions.

Air quality data are critical for understanding the health impacts of climate change, particularly in relation to respiratory and cardiovascular diseases. Rising temperatures can worsen air pollution levels by increasing ground-level ozone and fine particulate matter (PM2.5),⁶² both of which are linked to conditions such as asthma, chronic obstructive pulmonary disease (COPD) and cardiovascular illnesses.⁶³

Long-term monitoring of pollutant concentrations helps researchers track trends, assess health risks and design mitigation strategies. Urbanization, industrial emissions, wildfires and climate-driven changes in atmospheric circulation all contribute to fluctuating air quality, making continuous monitoring essential for public health responses.

Climate change is altering the distribution and intensity of vector-borne diseases, making long-term surveillance essential for tracking emerging health risks. Temperature, humidity and precipitation patterns directly influence mosquito and tick populations, affecting the spread of diseases such as malaria, dengue, Zika virus and Lyme disease.64 Warmer temperatures accelerate mosquito breeding cycles and expand their geographic range, while shifting rainfall patterns create new breeding sites.

Longitudinal disease surveillance allows researchers to predict outbreaks, assess climate-driven disease expansion and support the development of targeted public health interventions, such as vector control strategies and early warning systems.⁶⁵

Health surveillance systems provide critical data on the direct and indirect health impacts of climate change. These systems track morbidity and mortality trends linked to climate-sensitive health conditions, such as heat-related illnesses,⁶⁶ respiratory diseases,⁶⁷ waterborne infections,⁶⁸ and malnutrition.⁶⁹ By integrating climate variables with data on hospital admissions, disease registries and outbreak monitoring, researchers can assess long-term health trends and evaluate the effectiveness of adaptation strategies. Health surveillance is particularly important in detecting early warning signs of climate-driven health crises, enabling timely public health interventions and policy responses.

Remote sensing and geographic information systems (GIS) provide spatial and environmental data crucial for CCH monitoring. Satellite imagery and GIS-based analysis help track land use changes, extreme weather events, deforestation, desertification and water availability, all of which influence health outcomes.

Remote sensing is particularly valuable for monitoring vector-borne disease risks, air pollution levels and temperature fluctuations at local and global scales. By integrating GIS with health data, researchers can map disease hotspots, predict climate-driven health risks and support targeted interventions in vulnerable regions.⁷⁰

Advances in technology are transformative for CCH IR, enabling more precise, scalable and efficient data collection. By integrating remote sensing, GIS, automated health monitoring and AI-driven analytics, researchers can better track climate-driven health risks over time and evaluate the effectiveness of implementation strategies. By improving data accuracy, scalability and accessibility, these technologies help researchers assess what works, for whom and under what conditions. In Uganda, antimalarials and GIS mapping were used to reduce the risk of malaria spread following heavy rain and flooding (see case study).

Table 7 summarizes key technologies used in CCH research – listing specific tools or systems and their function.

Boyer, Bowen & Murray (2020) recommend the Consolidated Framework for Implementation Research (CFIR) as a useful framework for understanding and organizing “potential factors that can influence the integration of climate and health forecasted outlooks into health planning”.18 Using a CFIR framework can also help to identify and anticipate barriers to CCH interventions.¹⁸

Originating in quality improvement,² the PDSA cycle can be used to iteratively test improvements in complex health interventions such as CCH⁷³ – see Table 8 for a description of the cycle.

This implementation framework evaluates population health impacts of interventions. Researchers can use the five dimensions of the acronym – reach, effectiveness, adoption, implementation, maintenance – to pose questions interrogating the effectiveness of CCH programmes.^13,61 See Fig. 6.

The SCALE-up (successful climate change and health adaptation learning and evaluation) framework is a newly developed tool designed to assess the effectiveness of climate change interventions in the context of human health and health systems.⁷¹

Developed to assess positive, negative or neutral impacts of climate interventions on health and well-being, the SCALE-up framework comprises six steps (see Fig. 7):

1. Assess CCH risks and vulnerabilities.
2. Evaluate and select adaptation options.
3. Implementation.
4. Community and partner feedback.
5. Assess impact, revise interventions and share lessons learned.
6. Scale up and monitor impact.