Risk and vulnerability assessments have been used widely to determine the consequences of various stressors on the social and economic well-being of the communities exposed to the risk. Several studies have adopted this framework to evaluate the risk of climate change on farming communities [8, 25–27], coastal regions , disaster prone areas and health sector [29–31]. A few studies have also assessed the multi-factorial risk of malaria based on the risk and vulnerability assessment framework [32–34]. However, no study has adopted this approach to assess the social risk of malaria with respect to climate change in India.
The results of the study reveal that high malaria risk is found in the north-eastern and eastern parts of India as well as in some parts of the southern states of Maharashtra and Karnataka. High malaria risk in the north-eastern states can be attributed to high forest cover, poor housing materials, low per capita income, high burden of health centres and a large share of households that lack essential assets (such as telephone, computer, television etc.), which is coupled with a high climatic suitability for malaria. A risk and vulnerability assessment study undertaken in Rwanda also found that indicators such as poor housing wall materials, television ownership, poverty rate and clinic density had an excessive influence on the vulnerability index . In the present study, malaria risk was found highest in the states of Bihar, Uttar Pradesh and Jharkhand, which was mostly a result of low adaptive capacity and high sensitivity to the climatic hazard of malaria in these states. The strong influence of adaptive capacity was also observed in the states of Rajasthan and Jammu and Kashmir where vulnerability is significantly high despite low sensitivity to malaria, as a result of low adaptive capacity. Previous research conducted in Tanzania similarly reported that regions of low adaptive capacity had high vulnerabilities even when susceptibility to malaria was low . The southern states of Kerala and Tamil Nadu were found to have significantly low risk of malaria despite very high suitability. High adaptive capacity in this region acts as a buffer to high malaria hazard, thereby reducing the composite risk of malaria. This characteristic of the adaptive capacity dimension has been observed in a similar study undertaken in Rwanda, where high malaria suitability in the eastern lowlands was counter balanced by good socio-economic factors . Climate change is projected to increase malaria risk by 2030s in the north-eastern states where adaptive capacity was low. On the other hand, in districts with high adaptive capacity, climate change had only a marginal or no impact on malaria risk.
Comparison of the malaria risk map with the map of malaria endemicity (Fig. 8) provides an insight into the implications of malaria risk. It can be observed that the study correctly identifies the north eastern and eastern regions of India to be at higher risk for malaria, albeit with a few minor deviations. Malaria risk has been found to be highest in the state of Bihar, even though it has an API less than 0.1. A Joint Monitoring Mission for vector borne diseases conducted by NVBDCP and the World Health Organization (WHO) in 2014 reported significant deficiency in malaria surveillance in Bihar due to a large number of vacancies in field and supervisory staff and an Annual Blood Examination Rate (ABER) less than 1 since 2003 (desired ABER = 10) . This could be the reason for low API despite a very high malaria risk in Bihar. In Odisha, malaria risk appears to be understated in a few districts, when compared to the number of reported cases of malaria (Fig. 8). When observing the effect of individual factors, it is seen that low forest cover, low population density and high availability of essential assets could account for the lower risk in some districts of Odisha.
The results of the study are significantly dependent on the selection of suitable indicators due to which it is essential to discuss the relative importance of the indicators used. Most of the indicators used in the present study have also been used in similar risk and vulnerability assessments in the past [32–35, 37, 38]. One such study, conducted in East Africa, used elevation as a proxy indicator for immunity to malaria, and found that this resulted in high vulnerability in regions that are malaria free or have epidemic malaria . However, this was not deemed as an important indicator in the present study. In another malaria risk and vulnerability assessment, undertaken in Tanzania, a notable similarity between the malaria risk and malaria hazard maps was observed . This was attributed to the high weightage of the hazard variable used in the study (Entomological Inoculation Rate). However, such a similarity in the malaria hazard and malaria risk maps is not discernible in the present study even though the weight assigned to the hazard variable (Transmission window) was significantly high (0.25). For example, in the southern states of Kerala and Tamil Nadu very high malaria hazard does not lead to a significant malaria risk as it is offset by very high adaptive capacity. Therefore, adaptive capacity was found to bear greater influence on the composite malaria risk as opposed to the hazard dimension, which was more significant in previous research . This may be attributed to the fact that most parts of Tanzania were found unsuitable for malaria, due to which malaria hazard served as a limiting factor for malaria risk. On the other hand, in India most of the regions are climatically suitable for malaria and adaptive capacity serves as a limiting factor for malaria risk. This has important implications for malaria elimination as it demonstrates that targeting these socio-economic factors can prove crucial in attaining as well as sustaining malaria elimination.
The study highlights the importance of considering social and economic indicators to present a holistic view of malaria risk. While the hazard and exposure dimensions of risk cannot be changed, vulnerability of the communities to the malaria risk are dependent on several demographic and socio-economic indicators that are controllable. This includes indicators of sensitivity and adaptive capacity such as material of roof and wall of houses, literacy rate, per capita income and Health Centre population burden. Elimination of malaria may pose a larger challenge in regions with lower adaptive capacity as compared to regions with higher adaptive capacity. Therefore, targeting these indicators will be crucial in order to eliminate malaria and prevent any possibility of resurgence