At the time of writing, many people around the world are feeling the pain, disruption, and devastating health consequences driven by climate change. The world has been shocked by the widespread flooding in Europe and the consecutive catastrophic hurricanes in North America. Yet far less attention is given to the impacts of climate change in places where Médecins Sans Frontières (MSF) works, such as Central African Republic, Chad, Côte d’Ivoire, Democratic Republic of Congo, Myanmar, Niger, Nigeria, and South Sudan. In 2024, these populations have likewise been affected by devastating floods, many of them not for the first time.
Although immediate impacts like injury, displacement, and limited access to healthcare may be similar worldwide, the compounding crises that follow and the capacity to recover from these vary significantly. Individuals in low-resource and humanitarian settings face significant health threats while contributing the least to global emissions. These regions are often vulnerable to climate hazards and possess low adaptive capacity, increasing people’s susceptibility to the negative impacts of climate change.
In this brief, drawing on evidence from indicators in the 2024 report of the Lancet Countdown on Health and Climate Change, MSF teams present examples of how climate change and environmental degradation are making provision of assistance more difficult by amplifying health and humanitarian needs and by further complicating interventions. It also highlights activities that respond to the climate crisis using a three-pillar approach: mitigating MSF’s environmental footprint, adapting healthcare delivery and emergency response to the current and future realities of climate change, and advocating for those impacted.
The complexity of climate change and environmental degradation, coupled with highly politicised and siloed global response efforts often make it insufficiently clear to health and humanitarian implementing partners that every issue is part of a continuous process, where each component informs the others. In this brief, MSF staff outline six focus areas where teams are engaged in developing environmentally-informed health and humanitarian interventions, emphasising their interdependence, and how failure to act on one issue not only impedes progress on that specific component but also affects the entire sequence of subsequent actions.
There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high.
OBJECTIVES
The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak.
METHODS
We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design.
RESULTS
Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area.
CONCLUSION
Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.