This study was conducted on Manoka Island (Littoral Region of Cameroon) with the aim of analyzing climate change vulnerability and local adaptation strategies based on the local community’s perceptions and biophysical evidence. We used household surveys, focus group discussions, field observation, GIS, and remote sensing to collect data on variables of exposure, sensitivity, and adaptive capacity. Historical changes in rainfall and temperature, mangrove cover, and the occurrence of extreme climatic events were used as indicators of exposure. Property losses and income structure were used as indicators of sensitivity, while human, natural, social, financial, and physical assets represented adaptive capacity. 89 households were interviewed in the nine settlements of the island. Results show that Manoka Island is experiencing irregular rainfall patterns (with average annual values deviating from the mean by -1.9 to +1.8 mm) and increasing temperature (with annual values deviating from the mean by -1.2 to +3.12). The dynamics of the coastline between 1975 and 2017 using EPR show average setbacks of more than ±3 m/year, with erosion levels varying depending on the period and location. The number of households perceiving extreme climatic events like seasonal variability, flood, and rain storm was higher. From respondents’ perception, housing and health are the sectors most affected by climate change. The reported high dependence of households on fishing for income, their overall low livelihood diversification, and their poor access to climate information reported by 65% of respondents portray their poor adaptive capacity. Local response initiatives are ineffective and include among others constructing buildings on stilts and using car wheels to counter the advancement of seawater inland. The study concludes that households on Manoka Island are vulnerable to the impacts of climate change. Income diversification, mangrove reforestation, the development of sustainable supply chains for wood fuel, and sustainable fish smoking devices are the main pathways for adaptation planning in this area.
BACKGROUND: Epidemiological data of heart failure (HF) decompensation from the northern hemisphere suggests higher rates during winter. OBJECTIVES: We aimed to explore the seasonal variation in decompensated HF admission and mortality rates in a country with equatorial climate. METHODS: We conducted a retrospective cross-sectional study by chart review of the admission, discharge registries and patient files from 2016 to 2018 in the cardiology unit of the Yaound?? Central Hospital, Cameroon. Data was collected on HF morbidity and mortality from the registers and patients’ files. Corresponding seasonal climatic data was obtained from the meteorology office of the Cameroonian ministry of transports. Analysis of variance and Chi-square test were respectively used to compare the continuous and categorical data between across seasons. Correlation between continuous data was assess with the Spearman correlation. RESULTS: Decompensated HF accounted for 636 (36.2%) out 1755 cardiology unit admission and an 18% lethality rate. Decompensated HF admission, mortality and lethality rates were respectively 38.2%, 6.7% and 17.9% higher during the long rainy season (all P values >0.05). We observed a borderline-to-significant inverse linear continuous correlation between monthly temperatures and admission rate (r=-0.301; P=0.070), lethality rate (r=-0.361; P=0.030) and mortality rate (r=-0.385; P=0.020). There was no significant difference of the distribution of precipitating factors between seasons. CONCLUSION: Although statistically insignificant, decompensated HF admissions and mortality increase in rainy season where the temperature is lower in an equatorial climate.
Malaria is a critical health issue across the world and especially in Africa. Studies based on dynamical models helped to understand inter-linkages between this illness and climate. In this study, we evaluated the ability of the VECTRI community vector malaria model to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim, respectively. In addition, we simulated the model using five results of the dynamical downscaling of the regional climate model RCA4 within two time frames named near future (2035-2065) and far future (2071-2100), aiming to explore the potential effects of global warming on the malaria propagation over Cameroon. The evaluated metrics include the risk maps of the entomological inoculation rate (EIR) and the parasite ratio (PR). During the historical period (1985-2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m(-2)), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26-28 °C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision-makers and policy makers.
