Background
Nepal has made significant progress in reducing the incidence of malaria by more than 84% over the last decade [
1]. It has already achieved and exceeded the target of the Millennium Development Goals (MDGs) and universal coverage of malaria control interventions, and the Roll Back Malaria (RBM) targets of 2010 [
1‐
5]. However, an increasing proportion of
Plasmodium falciparum and imported malaria has been observed in the last decade [
1]. Although the analysis of malaria data from 31 malaria-risk districts has shown a significant decline of confirmed malaria and
Plasmodium vivax malaria incidences, no decline was found for
P. falciparum and clinically suspected malaria incidences [
1]. Malaria cases in Nepal are highly clustered and vector control interventions are not uniformly implemented. Accurate identification of malaria foci or clusters at the local level can greatly increase the effectiveness of vector control interventions whereas not identifying foci can cause effective control measures to fail [
6]. Hence, an accurate mapping of malaria-endemic foci at village development committee (VDC) or household levels improves the effectiveness and cost-effectiveness of vector control interventions.
Although about 84% (23 million) of the people in Nepal were estimated to be at risk of malaria in 2012, with 4% at high-risk [
7], the recent microstratification of malaria-risk areas in 2012 at the VDC level shows only approximately 13.02 million people (48%) living in malaria-endemic VDCs [
2]. Out of the estimated total population living in endemic areas, 0.98 million (3.6%) live in high-risk VDCs, 2.7 million (9.8%) live in moderate-risk VDCs, and 9.4 million (34.5%) in low-risk VDCs. Similarly, there are fewer districts classified as high- and moderate-risk (25 instead of 31) and the overall population living in VDCs at risk (estimated at 1,254 VDCs out of 3,972) is declining [
2], which indicates the progress made to eliminate malaria-endemic VDCs (foci) and to achieve the malaria elimination goal by 2026.
The high-risk areas consist of forest, forest fringe, foothills, river belts, hills, and river valleys. Malaria incidence has declined in districts regardless of whether vector control interventions (i.e., implementation of indoor residual spraying (IRS) and distribution of long-lasting insecticidal nets (LLINs)) have been implemented [
1]. Moreover, reports of malaria have increased in high-risk districts (Kailali, Nawalparasi, Dhanusha, and Mahottari) where vector control interventions were in place [
5]. The Global Fund to Fight AIDS, Tuberculosis and Malaria (GFATM) began supporting a malaria control programme in 13 high-risk districts in 2004 with support for rapid diagnostic test (RDT) kits, artemisinin combination therapy (ACT), LLINs and information, education and communication/behaviour change communication (IEC/BCC) for LLIN use [
5]. The distribution of LLINs started in 2006 in both districts.
Climatic factors such as temperature, humidity and rainfall play important roles in malaria transmission [
8]. Temperature is the major determinant of malaria risk [
9], but rainfall is important where temperature is not a limiting factor (e.g., in tropical areas of Africa and Bangladesh) [
10,
11]. Humidity is a direct product of temperature and rainfall and affects the distribution and longevity of vector mosquitoes, which in turn affects malaria transmission [
8]. The effect of climate change on malaria transmission in temperate regions and tropical highlands has been reported in many recent studies [
12‐
14].
The main aim of this study was to assess the spatio-temporal distribution of malaria and its association with climatic factors and vector control interventions in two high-risk districts of Nepal.
Discussion
Nepal has prepared for malaria pre-elimination since 2011 with the ambitious goal of malaria elimination by 2026. However, shifts of malaria hotspots to new VDCs in Morang district and stable malaria hotspots persist in specific VDCs of Kailali district, despite more than six years of continuous vector control interventions using IRS and LLINs, indicates that there are problems with the malaria elimination efforts in the country.
Despite declining trends of malaria in Morang district, malaria hotspots have shifted to new VDCs, which in the past had been regarded as low-risk VDCs so that vector control interventions were not in place. The hotspots were reported in eastern villages in 2009 since LLINs started to be distributed in high endemic VDCs of Morang district with one LLIN per two household persons policy in 2009. Malaria spread west in 2010 because eastern village hotspots disappeared and gradually shifted into forest and hill areas of the western part of the district. In contrast, despite vector control interventions since 2006, hotspots remained in the same VDCs in Kailali district. Both districts share a border with high-endemic districts in Nepal as well as with India. The scaling-up of LLINs started in Kailali and Morang districts in 2006. However, IRS activity was regular before and during the study period. Only LLINs is found to be associated with a decline of malaria incidence in the present study, which is consistent with findings from Bangladesh [
24] Rwanda [
33], and Zambia [
34]. In contrast to findings of this study, malaria incidence was associated with IRS coverage only in Botswana [
22]. These findings imply that vector control interventions with a low coverage, or those that are focussed on selected VDCs only, cannot break down malaria transmission in VDCs or districts bordering malaria-endemic areas because the presence of vectors, a suitable climate and the continuous import of malaria cases facilitate the transmission cycle. Furthermore, the higher incidence of malaria in the forest, forest fringe and foothills, especially in the high-risk VDCs of Kailali district where regular movement of people is high, indicates that forest-related malaria cannot be controlled using vector control interventions such as LLINs and IRS alone. For this, effective BCC strategies are needed to promote avoiding mosquito-man contact, e.g., by the use of mosquito repellents and/or protective clothing.
The proportion of imported malaria cases has almost constant over the years in both districts, consisting of more than 50% in Kailali district and 30% in Morang district (Table
1), which is consistent with previous findings [
1]. Imported malaria has appeared as a major challenge for many countries embarking on malaria elimination [
22,
35‐
37]. Large-scale migration within and outside the district, across the southern border of Nepal with India, displacement of the population after natural disasters such as floods, and the continuous introduction of malaria cases among adult migrant workers could be seen as factors for the persistence of malaria, especially in Kailali district. Hence, a cross-border malaria strategy that is well coordinated with malaria-endemic neighbouring counties such as India is very urgently needed. In the absence of the implementation of such a strategy, regular border screening among travellers should be carried out by establishing check posts for acute febrile illness at the border crossings, which may contribute to a reduction in malaria transmission among other benefits.
Climatic factors can be important for increasing the risk of malaria transmission especially in sub-tropical and temperate regions where the minimum temperature is a limiting factor for malaria transmission. The positive association of minimum and mean temperature with malaria incidence in the present study is consistent with the findings of many previous studies [
12‐
14,
38‐
45]. An increase in the minimum (and also mean) temperature increases mosquito abundance and biting rates and shortens the incubation period of
Plasmodium parasites, thereby increasing the malaria transmission cycle. Minimum temperature is the most influential environmental variable for malaria transmission since it occurs at night [
46]. This is because malaria vector mosquitoes remain active for biting during the night when people sleeping without bed nets are exposed to the bite of infected mosquitoes, resulting malaria infection. Furthermore, when exposed to high temperatures at night people usually do not cover themselves and some people (usually adult men) sleep outside the house under the trees to avoid the heat, which in turn increases the risk of malaria [
46‐
48]. These observations are consistent with the findings of this study that the incidence of malaria was higher among adults. In contrast, maximum temperature has a complex relationship to malaria because an increase in maximum temperature above a certain range interrupts mosquito and parasite development [
8,
14]. Similarly, the effect of rainfall on malaria incidence is complex. In areas where their breeding sites are produced by rainfall, increasing rainfall increases mosquito populations. However, too little rain, or drought, affects the mosquito life cycle as well as too much rainfall which can flush away the breeding places and thus decrease mosquito abundance [
11,
49‐
51].
No significant effect of the month of the year on malaria incidence was observed, which indicates a perennial distribution of malaria in both districts. Thus, IRS spraying in the pre-monsoon (April-May) and monsoon (July-August) seasons only may be ineffective for controlling malaria vectors. This observation can be explained by the fact that the minimum temperature increased rapidly in the terai region in all seasons [
52,
53], so that the transmission of malaria was possible even in winter. In contrast, an increase in mean temperature above 28°C [
14] mainly influenced by maximum temperature drastically reduces malaria transmission, particularly in the summer, which is the warmest season in Nepal. As a result of these two contrasting effects, coupled with an influx of infected migrant workers in the post-monsoon and winter seasons, the effect of the month on malaria incidence may be insignificant in this setting. Alternatively, there might be no effect of the month because temperature alone is sufficient to explain seasonal variation. Based on the association found between malaria incidence and temperature, malaria transmission in the temperate regions of Nepal can be predicted as revealed elsewhere by many previous studies [
12‐
14,
45].
The latest microstratification of malaria at the VDC level may be very useful for an effective utilization of limited resources. However, malaria microstratification should be updated regularly according to the progress with an aim to target new hotspots. As the country progresses towards elimination, finer scale mapping, i.e., at the ward or household level, is needed to identify residual foci [
54‐
56]. For example, a study from Bangladesh identifies stable malaria hotspots and risk factors at the household level which guide for cost-effective targeting of malaria intervention that may finally contribute to potential elimination of malaria from the country [
51]. The application of spatial decision support tools such as geographic position system (GPS), geographical information system (GIS) and mobile computing technology helps to identify spatial clusters of malaria transmission and provides effective monitoring, evaluation and surveillance tools to cope with the complexities that are associated with the spatial variability of malaria transmission and associated risk factors [
57].
This study, like other studies that use secondary data, has several limitations so the findings should be interpreted with caution. First, the analyses were based on routinely collected passive surveillance data from public health institutions only. As reported elsewhere [
2], malaria cases reported to the EDCD and malaria cases reported through the (HMIS) vary slightly. Monthly malaria data used in this study were aggregated at the district level and collected through the HMIS while yearly aggregated VDC-level malaria and vector control intervention data were collected from visits to each health institution of a district and from the EDCD, which resulted in slight differences in data in some years. Second, the observed association between malaria incidence with LLINs and IRS coverage and climatic factors is ecological and not at the level of individuals. Third, malaria incidence was calculated based on confirmed malaria cases at public health institutions only and therefore misses out on possible cases from the private healthcare sector. As a result, the results of this study may not represent the situation of malaria transmission at the population level. Furthermore, a single model could not be developed since the collected data of malaria, vector control interventions and climate factors were of different spatial (district and village level) and temporal (annual and monthly) scales. Despite these challenges, this study provides important information about the malaria situation at district and subdistrict (VDC) levels after the scaling-up of malaria control interventions from the GFAMT support, and this will be important for preparing the malaria pre-elimination phase in Nepal.
Conclusions
Despite a significant decline in malaria cases at the national level, an increasing trend of malaria incidence in Kailali district, with persistence of malaria cases in the same villages where vector control interventions had been in place, a shift of malaria hotspots to new villages in Morang district without vector control interventions, and a positive association of malaria incidence with temperature indicates worries about the elimination of malaria from the country. However, the malaria elimination goal can be achieved if hotspots of malaria can be identified accurately and vector control interventions such as LLIN coverage can be scaled up in the entire endemic districts rather than focusing only on selected VDCs. This is important because the movement of people within and between districts and across the border to India is high. Second, imported malaria cases should be controlled by establishing health check posts at the border crossings screening for people with acute febrile illness. Third, community-based prevalence surveys should be carried out to detect asymptomatic malaria cases, identify spatial clusters of malaria hotspots and determine the real malaria transmission situation at the population level. Fourth, data recording, reporting and surveillance systems should be strengthened and a case-based surveillance system should be started in all malaria-endemic districts. Fifth, microstratification of malaria transmission areas should be carried out integrating observed climatic data and high resolution remote sensing images, and differential diagnosis of any fever case coming from probable malaria transmission areas should be performed. Finally, continuous efforts are crucial to maintain and sustain the gains that have already been achieved.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
MD designed the study, compiled and analysed the data, and wrote the manuscript; RBO’H wrote the code for statistical analysis and contributed in manuscript writing; RCK provided meteorological data required for this study; GDT provided malaria and vector control intervention data required for this study; UK contributed to the writing and critically reviewed the manuscript and BA conceived the study and critically reviewed the manuscript. All authors read and approved the final manuscript.