Indoor residual spraying (IRS) is the mainstay of visceral leishmaniasis (VL) vector control efforts in India. Little is known about the impact of IRS controls on different types of households. Here we evaluate whether IRS using insecticides has the same residual and intervention effects for all types of households in a village. We also developed combined spatial risk maps and mosquito density analysis models based on household characteristics, pesticide sensitivity, and IRS status to examine the spatiotemporal distribution of vectors at the microscale level.
The study was conducted in two villages of Mahnar block in Vaishali district of Bihar. Control of VL vectors (P. argentipes) by IRS using two insecticides [dichlorodiphenyltrichloroethane (DDT 50%) and synthetic pyrethroids (SP 5%)] was evaluated. The temporal residual effectiveness of insecticides on different types of walls was assessed using the cone bioassay method as recommended by the World Health Organization. The sensitivity of native silverfish to insecticides was examined using an in vitro bioassay. Pre- and post-IRS mosquito densities in residences and animal shelters were monitored using light traps installed by the Centers for Disease Control from 6:00 p.m. to 6:00 a.m. The best-fitting model for mosquito density analysis was developed using multiple logistic regression analysis. GIS-based spatial analysis technology was used to map the distribution of vector pesticide sensitivity by household type, and household IRS status was used to explain the spatiotemporal distribution of silver shrimp.
Silver mosquitoes are very sensitive to SP (100%), but show high resistance to DDT, with a mortality rate of 49.1%. SP-IRS was reported to have better public acceptance than DDT-IRS among all types of households. Residual effectiveness varied across different wall surfaces; none of the insecticides met the World Health Organization’s IRS recommended duration of action. At all post-IRS time points, stink bug reductions due to SP-IRS were greater between household groups (i.e., sprayers and sentinels) than DDT-IRS. The combined spatial risk map shows that SP-IRS has a better control effect on mosquitoes than DDT-IRS in all household-type risk areas. Multilevel logistic regression analysis identified five risk factors that were strongly associated with silver shrimp density.
The results will provide a better understanding of IRS practices in controlling visceral leishmaniasis in Bihar, which may help guide future efforts to improve the situation.
Visceral leishmaniasis (VL), also known as kala-azar, is an endemic neglected tropical vector-borne disease caused by protozoan parasites of the genus Leishmania. In the Indian subcontinent (IS), where humans are the only reservoir host, the parasite (i.e. Leishmania donovani) is transmitted to humans through the bites of infected female mosquitoes (Phlebotomus argentipes) [1, 2]. In India, VL is predominantly found in four central and eastern states: Bihar, Jharkhand, West Bengal and Uttar Pradesh. Some outbreaks have also been reported in Madhya Pradesh (Central India), Gujarat (Western India), Tamil Nadu and Kerala (South India), as well as in the sub-Himalayan areas of northern India, including Himachal Pradesh and Jammu and Kashmir. 3]. Among the endemic states, Bihar is highly endemic with 33 districts affected by VL accounting for more than 70% of the total cases in India every year [4]. About 99 million people in the region are at risk, with an average annual incidence of 6,752 cases (2013-2017).
In Bihar and other parts of India, VL control efforts rely on three main strategies: early case detection, effective treatment, and vector control using indoor insecticide spraying (IRS) in homes and animal shelters [ 4 , 5 ]. As a side effect of antimalarial campaigns, the IRS successfully controlled VL in the 1960s using dichlorodiphenyltrichloroethane (DDT 50% WP, 1 g a.i./m2), and programmatic control successfully controlled VL in 1977 and 1992 [5 , 6]. However, recent studies have confirmed that silverbellied shrimp have developed widespread resistance to DDT [4,7,8]. In 2015, the National Vector Borne Disease Control Program (NVBDCP, New Delhi) switched IRS from DDT to synthetic pyrethroids (SP; alpha-cypermethrin 5% WP, 25 mg ai/m2) [7, 9]. The World Health Organization (WHO) has set a goal of eliminating VL by 2020 (i.e. <1 case per 10,000 people per year at street/block level) [10]. Several studies have shown that IRS is more effective than other vector control methods in minimizing sand fly densities [11,12,13]. A recent model also predicts that in high epidemic settings (i.e., pre-control epidemic rate of 5/10,000), an effective IRS covering 80% of households could achieve elimination goals one to three years earlier [14]. VL affects the poorest poor rural communities in endemic areas and their vector control relies solely on IRS, but the residual impact of this control measure on different types of households has never been studied in the field in intervention areas [ 15 , 16 ]. In addition, after intensive work to combat VL, the epidemic in some villages lasted for several years and turned into hot spots [17]. Therefore, it is necessary to evaluate the residual impact of IRS on mosquito density monitoring in different types of households. In addition, microscale geospatial risk mapping will help to better understand and control mosquito populations even after intervention. Geographic information systems (GIS) are a combination of digital mapping technologies that enable the storage, overlay, manipulation, analysis, retrieval and visualization of different sets of geographic environmental and socio-demographic data for various purposes [18, 19, 20]. . The global positioning system (GPS) is used to study the spatial position of components of the earth’s surface [21, 22]. GIS and GPS-based spatial modeling tools and techniques have been applied to several epidemiological aspects, such as spatial and temporal disease assessment and outbreak forecasting, implementation and evaluation of control strategies, interactions of pathogens with environmental factors, and spatial risk mapping. [20,23,24,25,26]. Information collected and derived from geospatial risk maps can facilitate timely and effective control measures.
This study assessed the residual effectiveness and effect of DDT and SP-IRS intervention at the household level under the National VL Vector Control Program in Bihar, India. Additional objectives were to develop a combined spatial risk map and mosquito density analysis model based on dwelling characteristics, insecticide vector susceptibility, and household IRS status to examine the hierarchy of spatiotemporal distribution of microscale mosquitoes.
The study was conducted in Mahnar block of Vaishali district on the northern bank of the Ganga (Fig. 1). Makhnar is a highly endemic area, with an average of 56.7 cases of VL per year (170 cases in 2012-2014), the annual incidence rate is 2.5–3.7 cases per 10,000 population; Two villages were selected: Chakeso as a control site (Fig. 1d1; no cases of VL in the last five years) and Lavapur Mahanar as an endemic site (Fig. 1d2; highly endemic, with 5 or more cases per 1000 people per year). over the past 5 years). Villages were selected based on three main criteria: location and accessibility (i.e. located on a river with easy access all year round), demographic characteristics and number of households (i.e. at least 200 households; Chaqueso has 202 and 204 households with average household size). 4.9 and 5.1 persons) and Lavapur Mahanar respectively) and household type (HT) and the nature of their distribution (i.e. randomly distributed mixed HT). Both study villages are located within 500 m of Makhnar town and the district hospital. The study showed that residents of the study villages were very actively involved in research activities. The houses in the training village [consisting of 1-2 bedrooms with 1 attached balcony, 1 kitchen, 1 bathroom and 1 barn (attached or detached)] consist of brick/mud walls and adobe floors, brick walls with lime cement plaster. and cement floors, unplastered and unpainted brick walls, clay floors and a thatched roof. The entire Vaishali region has a humid subtropical climate with a rainy season (July to August) and a dry season (November to December). The average annual precipitation is 720.4 mm (range 736.5-1076.7 mm), relative humidity 65±5% (range 16-79%), average monthly temperature 17.2-32.4°C. May and June are the warmest months (temperatures 39–44 °C), while January is the coldest (7–22 °C).
The map of the study area shows the location of Bihar on the map of India (a) and the location of Vaishali district on the map of Bihar (b). Makhnar Block (c) Two villages were selected for the study: Chakeso as the control site and Lavapur Makhnar as the intervention site.
As part of the National Kalaazar Control Programme, Bihar Society Health Board (SHSB) conducted two rounds of annual IRS during 2015 and 2016 (first round, February-March; second round, June-July)[4]. To ensure effective implementation of all IRS activities, a micro action plan has been prepared by the Rajendra Memorial Medical Institute (RMRIMS; Bihar), Patna, a subsidiary of the Indian Council of Medical Research (ICMR; New Delhi). nodal institute. IRS villages were selected based on two main criteria: history of cases of VL and retrodermal kala-azar (RPKDL) in the village (i.e., villages with 1 or more cases during any time period in the last 3 years, including the year of implementation). , non-endemic villages around “hot spots” (i.e. villages that have continuously reported cases for ≥ 2 years or ≥ 2 cases per 1000 people) and new endemic villages (no cases in the last 3 years) villages in the last year of the implementation year reported in [17]. Neighboring villages that implement the first round of national taxation, new villages are also included in the second round of the national taxation action plan. In 2015, two rounds of IRS using DDT (DDT 50% WP, 1 g ai/m2) were conducted in intervention study villages. Since 2016, IRS has been performed using synthetic pyrethroids (SP; alpha-cypermethrin 5% VP, 25 mg a.i./m2). Spraying was carried out using a Hudson Xpert pump (13.4 L) with a pressure screen, a variable flow valve (1.5 bar) and an 8002 flat jet nozzle for porous surfaces [27]. ICMR-RMRIMS, Patna (Bihar) monitored IRS at household and village level and provided preliminary information about IRS to villagers through microphones within the first 1-2 days. Each IRS team is equipped with a monitor (provided by RMRIMS) to monitor the performance of the IRS team. Ombudsmen, along with IRS teams, are deployed to all households to inform and reassure heads of households about the beneficial effects of the IRS. During two rounds of IRS surveys, overall household coverage in the study villages reached at least 80% [4]. Spraying status (i.e., no spraying, partial spraying, and full spraying; defined in Additional file 1: Table S1) was recorded for all households in the intervention village during both rounds of IRS.
The study was conducted from June 2015 to July 2016. The IRS used disease centers for pre-intervention (i.e., 2 weeks pre-intervention; baseline survey) and post-intervention (i.e., 2, 4, and 12 weeks post-intervention; follow-up surveys) monitoring, density control, and sand fly prevention in each IRS round. in each household One night (i.e. from 18:00 to 6:00) light trap [28]. Light traps have been installed in bedrooms and animal shelters. In the village where the intervention study was conducted, 48 households were tested for sand fly density before IRS (12 households per day for 4 consecutive days up to the day before IRS day). 12 were selected for each of the four main groups of households (i.e. plain clay plaster (PMP), cement plaster and lime cladding (CPLC) households, brick unplastered and unpainted (BUU) and thatched roof (TH) households). Thereafter, only 12 households (out of 48 pre-IRS households) were selected to continue collecting mosquito density data after the IRS meeting. According to WHO recommendations, 6 households were selected from the intervention group (households receiving IRS treatment) and the sentinel group (households in intervention villages, those owners who refused IRS permission) [28]. Among the control group (households in neighboring villages that did not undergo IRS due to lack of VL), only 6 households were selected to monitor mosquito densities before and after two IRS sessions. For all three mosquito density monitoring groups (i.e. intervention, sentinel and control), households were selected from three risk level groups (i.e. low, medium and high; two households from each risk level) and HT risk characteristics were classified (modules and structures are shown in Table 1 and Table 2, respectively) [29, 30]. Two households per risk level were selected to avoid biased mosquito density estimates and comparisons between groups. In the intervention group, post-IRS mosquito densities were monitored in two types of IRS households: fully treated (n = 3; 1 household per risk group level) and partially treated (n = 3; 1 household per risk group level). ). risk group).
All field-caught mosquitoes collected in test tubes were transferred to the laboratory, and the test tubes were killed using cotton wool soaked in chloroform. Silver sandflies were sexed and separated from other insects and mosquitoes based on morphological characteristics using standard identification codes [31]. All male and female silver shrimp were then canned separately in 80% alcohol. Mosquito density per trap/night was calculated using the following formula: total number of mosquitoes collected/number of light traps set per night. The percentage change in mosquito abundance (SFC) due to IRS using DDT and SP was estimated using the following formula [32]:
where A is the baseline mean SFC for intervention households, B is the IRS mean SFC for intervention households, C is the baseline mean SFC for control/sentinel households, and D is the mean SFC for IRS control/sentinel households households.
The intervention effect results, recorded as negative and positive values, indicate a decrease and increase in SFC after IRS, respectively. If SFC after IRS remained the same as baseline SFC, the intervention effect was calculated as zero.
According to the World Health Organization Pesticide Evaluation Scheme (WHOPES), the sensitivity of native silverleg shrimp to the pesticides DDT and SP was assessed using standard in vitro bioassays [33]. Healthy and unfed female silver shrimp (18–25 SF per group) were exposed to pesticides obtained from Universiti Sains Malaysia (USM, Malaysia; coordinated by the World Health Organization) using the World Health Organization Pesticide Sensitivity Test Kit [4,9, 33,34]. Each set of pesticide bioassays was tested eight times (four test replicates, each run simultaneously with the control). Control tests were carried out using paper pre-impregnated with risella (for DDT) and silicone oil (for SP) provided by USM. After 60 minutes of exposure, mosquitoes were placed in WHO tubes and provided with absorbent cotton wool soaked in a 10% sugar solution. The number of mosquitoes killed after 1 hour and final mortality after 24 hours were observed. Resistance status is described according to World Health Organization guidelines: mortality of 98–100% indicates susceptibility, 90–98% indicates possible resistance requiring confirmation, and <90% indicates resistance [33, 34]. Because mortality in the control group ranged from 0 to 5%, no mortality adjustment was performed.
The bioefficacy and residual effects of insecticides on native termites under field conditions were assessed. In three intervention households (one each with plain clay plaster or PMP, cement plaster and lime coating or CPLC, unplastered and unpainted brick or BUU) at 2, 4 and 12 weeks after spraying. A standard WHO bioassay was performed on cones containing light traps. established [27, 32]. Household heating was excluded due to uneven walls. In each analysis, 12 cones were used across all experimental homes (four cones per home, one for each wall surface type). Attach cones to each wall of the room at different heights: one at head level (from 1.7 to 1.8 m), two at waist level (from 0.9 to 1 m) and one below the knee (from 0.3 to 0 .5 m). Ten unfed female mosquitoes (10 per cone; collected from a control plot using an aspirator) were placed in each WHO plastic cone chamber (one cone per household type) as controls. After 30 minutes of exposure, carefully remove mosquitoes from it; conical chamber using an elbow aspirator and transfer them into WHO tubes containing 10% sugar solution for feeding. Final mortality after 24 hours was recorded at 27 ± 2°C and 80 ± 10% relative humidity. Mortality rates with scores between 5% and 20% are adjusted using the Abbott formula [27] as follows:
where P is the adjusted mortality, P1 is the observed mortality percentage, and C is the control mortality percentage. Trials with control mortality >20% were discarded and rerun [27, 33].
A comprehensive household survey was conducted in the intervention village. The GPS location of each household was recorded along with its design and material type, dwelling, and intervention status. The GIS platform has developed a digital geodatabase that includes boundary layers at the village, district, district and state levels. All household locations are geotagged using village-level GIS point layers, and their attribute information is linked and updated. At each household site, risk was assessed based on HT, insecticide vector susceptibility, and IRS status (Table 1) [11, 26, 29, 30]. All household location points were then converted into thematic maps using inverse distance weighting (IDW; resolution based on average household area of 6 m2, power 2, fixed number of surrounding points = 10, using variable search radius, low pass filter). and cubic convolution mapping) spatial interpolation technology [35]. Two types of thematic spatial risk maps were created: HT-based thematic maps and pesticide vector sensitivity and IRS status (ISV and IRSS) thematic maps. The two thematic risk maps were then combined using weighted overlay analysis [36]. During this process, raster layers were reclassified into general preference classes for different risk levels (i.e., high, medium, and low/no risk). Each reclassified raster layer was then multiplied by the weight assigned to it based on the relative importance of parameters that support mosquito abundance (based on prevalence in study villages, mosquito breeding sites, and resting and feeding behavior) [26, 29]. , 30, 37]. Both subject risk maps were weighted 50:50 as they contributed equally to mosquito abundance (Additional file 1: Table S2). By summing the weighted overlay thematic maps, a final composite risk map is created and visualized on the GIS platform. The final risk map is presented and described in terms of Sand Fly Risk Index (SFRI) values calculated using the following formula:
In the formula, P is the risk index value, L is the overall risk value for each household’s location, and H is the highest risk value for a household in the study area. We prepared and performed GIS layers and analysis using ESRI ArcGIS v.9.3 (Redlands, CA, USA) to create risk maps.
We conducted multiple regression analyzes to examine the combined effects of HT, ISV, and IRSS (as described in Table 1) on house mosquito densities (n = 24). Housing characteristics and risk factors based on the IRS intervention recorded in the study were treated as explanatory variables, and mosquito density was used as the response variable. Univariate Poisson regression analyzes were performed for each explanatory variable associated with sandfly density. During univariate analysis, variables that were not significant and had a P value greater than 15% were removed from the multiple regression analysis. To examine interactions, interaction terms for all possible combinations of significant variables (found in univariate analysis) were simultaneously included in multiple regression analysis, and nonsignificant terms were removed from the model in a stepwise manner to create the final model.
Household-level risk assessment was carried out in two ways: household-level risk assessment and combined spatial assessment of risk areas on a map. Household-level risk estimates were estimated using correlation analysis between household risk estimates and sand fly densities (collected from 6 sentinel households and 6 intervention households; weeks before and after IRS implementation). Spatial risk zones were estimated using the average number of mosquitoes collected from different households and compared between risk groups (i.e. low, medium and high risk zones). In each IRS round, 12 households (4 households in each of three levels of risk zones; nightly collections are conducted every 2, 4, and 12 weeks after IRS) were randomly selected to collect mosquitoes to test the comprehensive risk map. The same household data (i.e. HT, VSI, IRSS and mean mosquito density) were used to test the final regression model. A simple correlation analysis was conducted between field observations and model-predicted household mosquito densities.
Descriptive statistics such as mean, minimum, maximum, 95% confidence intervals (CI) and percentages were calculated to summarize entomological and IRS-related data. Average number/density and mortality of silver bugs (insecticidal agent residues) using parametric tests [paired samples t-test (for normally distributed data)] and non-parametric tests (Wilcoxon signed rank) to compare effectiveness between surface types in homes (i.e. e., BUU vs. CPLC, BUU vs. PMP, and CPLC vs. PMP) test for non-normally distributed data). All analyzes were performed using SPSS v.20 software (SPSS Inc., Chicago, IL, USA).
Household coverage in intervention villages during the IRS DDT and SP rounds was calculated. A total of 205 households received IRS in each round, including 179 households (87.3%) in the DDT round and 194 households (94.6%) in the SP round for VL vector control. The proportion of households fully treated with pesticides was higher during SP-IRS (86.3%) than during DDT-IRS (52.7%). The number of households that opted out of IRS during DDT was 26 (12.7%) and the number of households that opted out of IRS during SP was 11 (5.4%). During the DDT and SP rounds, the number of partially treated households registered was 71 (34.6% of total treated households) and 17 households (8.3% of total treated households), respectively.
According to WHO pesticide resistance guidelines, the silver shrimp population at the intervention site was fully susceptible to alpha-cypermethrin (0.05%) as the average mortality reported during the trial (24 hours) was 100%. The observed knockdown rate was 85.9% (95% CI: 81.1–90.6%). For DDT, the knockdown rate at 24 hours was 22.8% (95% CI: 11.5–34.1%), and the mean electronic test mortality was 49.1% (95% CI: 41.9–56.3 %). The results showed that silverfoots developed complete resistance to DDT at the intervention site.
In table Table 3 summarizes the results of bioanalysis of cones for different types of surfaces (different time intervals after IRS) treated with DDT and SP. Our data showed that after 24 hours, both insecticides (BUU vs. CPLC: t(2)= – 6.42, P = 0.02; BUU vs. PMP: t(2) = 0.25, P = 0.83; CPLC vs PMP: t(2)= 1.03, P = 0.41 (for DDT-IRS and BUU) CPLC: t(2)= − 5.86, P = 0.03 and PMP: t(2) = 1.42, P = 0.29; IRS, CPLC and PMP: t(2) = 3.01, P = 0.10 and SP: t(2) = 9.70, P = 0.01; mortality rates decreased steadily over time. For SP-IRS: 2 weeks post-spray for all wall types (i.e. 95.6% overall) and 4 weeks post-spray for CPLC walls only (i.e. 82.5). In the DDT group, mortality was consistently below 70% for all wall types at all time points after the IRS bioassay. The average experimental mortality rates for DDT and SP after 12 weeks of spraying were 25.1% and 63.2%, respectively. three surface types, the highest mean mortality rates with DDT were 61.1% (for PMP 2 weeks after IRS), 36.9% (for CPLC 4 weeks after IRS), and 28.9% (for CPLC 4 weeks after the IRS). Minimum rates are 55% (for BUU, 2 weeks after IRS), 32.5% (for PMP, 4 weeks after IRS) and 20% (for PMP, 4 weeks after IRS); US IRS). For SP, the highest mean mortality rates for all surface types were 97.2% (for CPLC, 2 weeks after IRS), 82.5% (for CPLC, 4 weeks after IRS), and 67.5% (for CPLC, 4 weeks after IRS). 12 weeks after IRS). US IRS). weeks after IRS); the lowest rates were 94.4% (for BUU, 2 weeks after IRS), 75% (for PMP, 4 weeks after IRS), and 58.3% (for PMP, 12 weeks after IRS). For both insecticides, mortality on PMP-treated surfaces varied more rapidly over time intervals than on CPLC- and BUU-treated surfaces.
Table 4 summarizes the intervention effects (i.e., post-IRS changes in mosquito abundance) of the DDT- and SP-based IRS rounds (Additional file 1: Figure S1). For DDT-IRS, the percentage reductions in silverlegged beetles after the IRS interval were 34.1% (at 2 weeks), 25.9% (at 4 weeks), and 14.1% (at 12 weeks). For SP-IRS, the reduction rates were 90.5% (at 2 weeks), 66.7% (at 4 weeks), and 55.6% (at 12 weeks). The largest declines in silver shrimp abundance in sentinel households during the DDT and SP IRS reporting periods were 2.8% (at 2 weeks) and 49.1% (at 2 weeks), respectively. During the SP-IRS period, the decline (before and after) of white-bellied pheasants was similar in spraying households (t(2)= – 9.09, P < 0.001) and sentinel households (t(2) = – 1.29, P = 0.33). Higher compared to DDT-IRS at all 3 time intervals after IRS. For both insecticides, silver bug abundance increased in sentinel households 12 weeks after IRS (i.e., 3.6% and 9.9% for SP and DDT, respectively). During SP and DDT following IRS meetings, 112 and 161 silver shrimp were collected from sentinel farms, respectively.
No significant differences in silver shrimp density were observed between household groups (i.e. spray vs sentinel: t(2)= – 3.47, P = 0.07; spray vs control: t(2) = – 2.03 , P = 0.18; sentinel vs. control: during IRS weeks after DDT, t(2) = − 0.59, P = 0.62). In contrast, significant differences in silver shrimp density were observed between the spray group and the control group (t(2) = – 11.28, P = 0.01) and between the spray group and the control group (t(2) = – 4, 42, P = 0.05). IRS a few weeks after SP. For SP-IRS, no significant differences were observed between sentinel and control families (t(2)= -0.48, P = 0.68). Figure 2 shows the average silver-bellied pheasant densities observed on farms fully and partially treated with IRS wheels. There were no significant differences in densities of fully managed pheasants between fully and partially managed households (mean 7.3 and 2.7 per trap/night). DDT-IRS and SP-IRS, respectively), and some households were sprayed with both insecticides (mean 7.5 and 4.4 per night for DDT-IRS and SP-IRS, respectively) (t(2) ≤ 1.0, P > 0.2). However, silver shrimp densities in fully and partially sprayed farms differed significantly between the SP and DDT IRS rounds (t(2) ≥ 4.54, P ≤ 0.05).
Estimated mean density of silver-winged stink bugs in fully and partially treated households in Mahanar village, Lavapur, during the 2 weeks before IRS and 2, 4 and 12 weeks after the IRS, DDT and SP rounds.
A comprehensive spatial risk map (Lavapur Mahanar village; total area: 26,723 km2) was developed to identify low, medium and high spatial risk zones to monitor the emergence and resurgence of silver shrimp before and several weeks after the implementation of IRS (Figs. 3, 4). . . The highest risk score for households during the creation of the spatial risk map was rated as “12” (i.e., “8” for HT-based risk maps and “4” for VSI- and IRSS-based risk maps). The minimum calculated risk score is “zero” or “no risk” except for DDT-VSI and IRSS maps which have a minimum score of 1. The HT based risk map showed that a large area (i.e. 19,994.3 km2; 74.8%) of Lavapur Mahanar village is a high-risk area where residents are most likely to encounter and re-emerge mosquitoes. Area coverage varies between high (DDT 20.2%; SP 4.9%), medium (DDT 22.3%; SP 4.6%) and low/no risk (DDT 57.5%; SP 90.5) zones %) ( t (2) = 12.7, P < 0.05) between the risk graphs of DDT and SP-IS and IRSS (Fig. 3, 4). The final composite risk map developed showed that SP-IRS had better protective capabilities than DDT-IRS across all levels of HT risk areas. The high risk area for HT was reduced to less than 7% (1837.3 km2) after SP-IRS and most of the area (i.e. 53.6%) became low risk area. During the DDT-IRS period, the percentage of high- and low-risk areas assessed by the combined risk map was 35.5% (9498.1 km2) and 16.2% (4342.4 km2), respectively. Sand fly densities measured in treated and sentinel households before and several weeks after IRS implementation were plotted and visualized on a combined risk map for each round of IRS (i.e., DDT and SP) (Figs. 3, 4). There was good agreement between household risk scores and average silver shrimp densities recorded before and after IRS (Fig. 5). The R2 values (P < 0.05) of the consistency analysis calculated from the two rounds of IRS were: 0.78 2 weeks before DDT, 0.81 2 weeks after DDT, 0.78 4 weeks after DDT, 0.83 after DDT- DDT 12 weeks, DDT Total after SP was 0.85, 0.82 2 weeks before SP, 0.38 2 weeks after SP, 0.56 4 weeks after SP, 0.81 12 weeks after SP and 0.79 2 weeks after SP overall (Additional file 1: Table S3). Results showed that the effect of the SP-IRS intervention on all HTs was enhanced over the 4 weeks following IRS. DDT-IRS remained ineffective for all HTs at all time points after IRS implementation. The results of the field assessment of the integrated risk map area are summarized in Table 5. For IRS rounds, mean silverbellied shrimp abundance and percentage of total abundance in high-risk areas (i.e., >55%) was higher than in low- and medium-risk areas at all post-IRS time points. The locations of entomological families (i.e. those selected for mosquito collection) are mapped and visualized in Additional file 1: Figure S2.
Three types of GIS based spatial risk maps (i.e. HT, IS and IRSS and combination of HT, IS and IRSS) to identify stink bug risk areas before and after DDT-IRS in Mahnar village, Lavapur, Vaishali district (Bihar)
Three types of GIS-based spatial risk maps (i.e. HT, IS and IRSS and combination of HT, IS and IRSS) to identify silver spotted shrimp risk areas (compared to Kharbang)
The impact of DDT-(a, c, e, g, i) and SP-IRS (b, d, f, h, j) on different levels of household type risk groups was calculated by estimating the “R2” between household risks. Estimation of household indicators and average density of P. argentipes 2 weeks before IRS implementation and 2, 4 and 12 weeks after IRS implementation in Lavapur Mahnar village, Vaishali district, Bihar
Table 6 summarizes the results of the univariate analysis of all risk factors affecting flake density. All risk factors (n = 6) were found to be significantly associated with household mosquito density. It was observed that the significance level of all relevant variables produced P values less than 0.15. Thus, all explanatory variables were retained for multiple regression analysis. The best-fitting combination of the final model was created based on five risk factors: TF, TW, DS, ISV, and IRSS. Table 7 lists details of the parameters selected in the final model, as well as adjusted odds ratios, 95% confidence intervals (CIs), and P values. The final model is highly significant, with an R2 value of 0.89 (F(5)=27 .9, P<0.001).
TR was excluded from the final model because it was least significant (P = 0.46) with the other explanatory variables. The developed model was used to predict sand fly densities based on data from 12 different households. Validation results showed a strong correlation between mosquito densities observed in the field and mosquito densities predicted by the model (r = 0.91, P < 0.001).
The goal is to eliminate VL from endemic states of India by 2020 [10]. Since 2012, India has made significant progress in reducing the incidence and mortality of VL [10]. The switch from DDT to SP in 2015 was a major change in the history of IRS in Bihar, India [38]. To understand the spatial risk of VL and the abundance of its vectors, several macro-level studies have been conducted. However, although the spatial distribution of VL prevalence has received increasing attention across the country, little research has been conducted at the micro level. Moreover, at the micro level, data is less consistent and more difficult to analyze and understand. To the best of our knowledge, this study is the first report to evaluate the residual efficacy and intervention effect of IRS using insecticides DDT and SP among HTs under the National VL Vector Control Program in Bihar (India). This is also the first attempt to develop a spatial risk map and mosquito density analysis model to reveal the spatiotemporal distribution of mosquitoes at the microscale under IRS intervention conditions.
Our results showed that household adoption of SP-IRS was high in all households and that most households were fully processed. The bioassay results showed that silver sand flies in the study village were highly sensitive to beta-cypermethrin but rather low to DDT. The average mortality rate of silver shrimp from DDT is less than 50%, indicating a high level of resistance to DDT. This is consistent with the results of previous studies conducted at different times in different villages of VL-endemic states of India, including Bihar [8,9,39,40]. In addition to pesticide sensitivity, the residual effectiveness of pesticides and the effects of intervention are also important information. The duration of residual effects is important for the programming cycle. It determines the intervals between rounds of IRS so that the population remains protected until the next spray. Cone bioassay results revealed significant differences in mortality between wall surface types at different time points after IRS. Mortality on DDT-treated surfaces was always below the WHO satisfactory level (i.e., ≥80%), whereas on SP-treated walls, mortality remained satisfactory until the fourth week after IRS; From these results, it is clear that although silverleg shrimp found in the study area are very sensitive to SP, the residual effectiveness of SP varies depending on HT. Like DDT, SP also does not meet the duration of effectiveness specified in WHO guidelines [41, 42]. This inefficiency may be due to poor implementation of the IRS (i.e. moving the pump at the appropriate speed, distance from the wall, discharge rate and size of water droplets and their deposition on the wall), as well as unwise use of pesticides (i.e. solution preparation) [11,28,43]. However, since this study was conducted under strict monitoring and control, another reason for not meeting the World Health Organization recommended expiration date could be the quality of the SP (i.e., the percentage of active ingredient or “AI”) that constitutes the QC.
Of the three surface types used to evaluate pesticide persistence, significant differences in mortality were observed between BUU and CPLC for two pesticides. Another new finding is that CPLC showed better residual performance in almost all time intervals after spraying followed by BUU and PMP surfaces. However, two weeks after IRS, PMP recorded the highest and second highest mortality rates from DDT and SP, respectively. This result indicates that the pesticide deposited on the surface of the PMP does not persist for a long time. This difference in the effectiveness of pesticide residues between wall types may be due to a variety of reasons, such as the composition of the wall chemicals (increased pH causing some pesticides to break down quickly), absorption rate (higher on soil walls), availability of bacterial decomposition and the rate of degradation of wall materials, as well as temperature and humidity [44, 45, 46, 47, 48, 49]. Our results support several other studies on the residual effectiveness of insecticide-treated surfaces against various disease vectors [45, 46, 50, 51].
Estimates of mosquito reduction in treated households showed that SP-IRS was more effective than DDT-IRS in controlling mosquitoes at all post-IRS intervals (P < 0.001). For the SP-IRS and DDT-IRS rounds, the rates of decline for treated households from 2 to 12 weeks were 55.6-90.5% and 14.1-34.1%, respectively. These results also showed that significant effects on P. argentipes abundance in sentinel households were observed within 4 weeks of IRS implementation; argentipes increased in both rounds of IRS 12 weeks after IRS; However, there was no significant difference in the number of mosquitoes in sentinel households between the two rounds of IRS (P = 0.33). Results from statistical analyzes of silver shrimp densities between household groups in each round also showed no significant differences in DDT across all four household groups (i.e., sprayed vs. sentinel; sprayed vs. control; sentinel vs. control; complete vs. partial). ). Two family groups IRS and SP-IRS (i.e., sentinel vs. control and full vs. partial). However, significant differences in silver shrimp densities between the DDT and SP-IRS rounds were observed in partially and fully sprayed farms. This observation, combined with the fact that intervention effects were calculated multiple times after IRS, suggests that SP is effective for mosquito control in homes that are partially or fully treated, but not untreated. However, although there were no statistically significant differences in the number of mosquitoes in sentinel houses between the DDT-IRS and SP IRS rounds, the average number of mosquitoes collected during the DDT-IRS round was lower compared to the SP-IRS round. .Quantity exceeds quantity. This result suggests that the vector-sensitive insecticide with the highest IRS coverage among the household population may have a population effect on mosquito control in households that were not sprayed. According to the results, SP had a better preventive effect against mosquito bites than DDT in the first days after IRS. In addition, alpha-cypermethrin belongs to the SP group, has contact irritation and direct toxicity to mosquitoes and is suitable for IRS [51, 52]. This may be one of the main reasons why alpha-cypermethrin has minimal effect in outposts. Another study [52] found that although alpha-cypermethrin demonstrated existing responses and high knockdown rates in laboratory assays and in huts, the compound did not produce a repellent response in mosquitoes under controlled laboratory conditions. cabin. website.
In this study, three types of spatial risk maps were developed; Household-level and area-level spatial risk estimates were assessed through field observations of silverleg shrimp densities. Analysis of risk zones based on HT showed that majority of village areas (>78%) of Lavapur-Mahanara are at the highest level of risk of sandfly occurrence and re-emergence. This is probably the main reason why Rawalpur Mahanar VL is so popular. The overall ISV and IRSS, as well as the final combined risk map, were found to produce a lower percentage of areas under high-risk areas during the SP-IRS round (but not the DDT-IRS round). After SP-IRS, large areas of high and moderate risk zones based on GT were converted to low risk zones (i.e. 60.5%; combined risk map estimates), which is almost four times lower (16.2% ) than DDT. – The situation is on the IRS portfolio risk chart above. This result indicates that IRS is the right choice for mosquito control, but the degree of protection depends on the quality of the insecticide, sensitivity (to the target vector), acceptability (at the time of IRS) and its application;
Household risk assessment results showed good agreement (P < 0.05) between risk estimates and the density of silverleg shrimp collected from different households. This suggests that the identified household risk parameters and their categorical risk scores are well suited for estimating local abundance of silver shrimp. The R2 value of the post-IRS DDT agreement analysis was ≥ 0.78, which was equal to or greater than the pre-IRS value (i.e., 0.78). The results showed that DDT-IRS was effective in all HT risk zones (i.e., high, medium, and low). For the SP-IRS round, we found that the value of R2 fluctuated in the second and fourth weeks after IRS implementation, the values two weeks before IRS implementation and 12 weeks after IRS implementation were almost the same; This result reflects the significant effect of SP-IRS exposure on mosquitoes, which showed a decreasing trend with time interval after IRS. The impact of SP-IRS has been highlighted and discussed in previous chapters.
Results from a field audit of the pooled map’s risk zones showed that during the IRS round, the highest numbers of silver shrimp were collected in high-risk zones (i.e., >55%), followed by medium- and low-risk zones. In summary, GIS-based spatial risk assessment has proven to be an effective decision-making tool for aggregating different layers of spatial data individually or in combination to identify sand fly risk areas. The developed risk map provides a comprehensive understanding of the pre- and post-intervention conditions (i.e., household type, IRS status, and intervention effects) in the study area that require immediate action or improvement, especially at the micro level. A very popular situation. In fact, several studies have used GIS tools to map the risk of vector breeding sites and the spatial distribution of diseases at the macro level [ 24 , 26 , 37 ].
Housing characteristics and risk factors for IRS-based interventions were statistically assessed for use in silver shrimp density analyses. Although all six factors (i.e., TF, TW, TR, DS, ISV, and IRSS) were significantly associated with local abundance of silverleg shrimp in univariate analyses, only one of them was selected in the final multiple regression model out of five. The results show that the captive management characteristics and intervention factors of IRS TF, TW, DS, ISV, IRSS, etc. in the study area are suitable for monitoring the emergence, recovery and reproduction of silver shrimp. In multiple regression analysis, TR was not found to be significant and was therefore not selected in the final model. The final model was highly significant, with the selected parameters explaining 89% of silverleg shrimp density. Model accuracy results showed a strong correlation between predicted and observed silver shrimp densities. Our results also support earlier studies that discussed socioeconomic and housing risk factors associated with VL prevalence and spatial distribution of vector in rural Bihar [15, 29].
In this study, we did not evaluate pesticide deposition on sprayed walls and the quality (i.e.) of the pesticide used for IRS. Variations in pesticide quality and quantity can affect mosquito mortality and the effectiveness of IRS interventions. Thus, estimated mortality among surface types and intervention effects among household groups may differ from actual results. Taking these points into account, a new study can be planned. The assessment of the total area at risk (using GIS risk mapping) of the study villages includes open areas between villages, which influences the classification of risk zones (i.e. identification of zones) and extends to different risk zones; However, this study was conducted at a micro level, so vacant land has only a minor impact on the classification of risk areas; In addition, identifying and assessing different risk zones within the total area of the village can provide an opportunity to select areas for future new housing construction (especially the selection of low-risk zones). Overall, the results of this study provide a variety of information that has never been studied at the microscopic level before. Most importantly, the spatial representation of the village risk map helps to identify and group households in different risk areas, compared with traditional ground surveys, this method is simple, convenient, cost-effective and less labor-intensive, providing information to decision makers.
Our results indicate that native silverfish in the study village have developed resistance (i.e., are highly resistant) to DDT, and mosquito emergence was observed immediately after IRS; Alpha-cypermethrin appears to be the right choice for IRS control of VL vectors due to its 100% mortality and better intervention efficacy against silverflies, as well as its better community acceptance compared to DDT-IRS. However, we found that mosquito mortality on SP-treated walls varied depending on the surface type; poor residual efficacy was observed and the WHO recommended time after IRS was not achieved. This study provides a good starting point for discussion, and its results require further study to identify the real root causes. The predictive accuracy of the sand fly density analysis model showed that a combination of housing characteristics, insecticide sensitivity of vectors and IRS status can be used to estimate sand fly densities in VL endemic villages in Bihar. Our study also shows that combined GIS-based spatial risk mapping (macro level) can be a useful tool for identifying risk areas to monitor the emergence and re-emergence of sand masses before and after IRS meetings. In addition, spatial risk maps provide a comprehensive understanding of the extent and nature of risk areas at different levels, which cannot be studied through traditional field surveys and conventional data collection methods. Microspatial risk information collected through GIS maps can help scientists and public health researchers develop and implement new control strategies (i.e. single intervention or integrated vector control) to reach different groups of households depending on the nature of risk levels . Additionally, the risk map helps optimize the allocation and use of control resources at the right time and place to improve program effectiveness.
World Health Organization. Neglected tropical diseases, hidden successes, new opportunities. 2009. http://apps.who.int/iris/bitstream/10665/69367/1/WHO_CDS_NTD_2006.2_eng.pdf. Date accessed: March 15, 2014
World Health Organization. Control of leishmaniasis: report of the meeting of the World Health Organization Expert Committee on Leishmaniasis Control. 2010. http://apps.who.int/iris/bitstream/10665/44412/1/WHO_TRS_949_eng.pdf. Date accessed: March 19, 2014
Singh S. Changing trends in the epidemiology, clinical presentation and diagnosis of leishmania and HIV coinfection in India. Int J Inf Dis. 2014;29:103–12.
National Vector Borne Disease Control Program (NVBDCP). Accelerate the Kala Azar destruction program. 2017. https://www.who.int/leishmaniasis/resources/Accelerated-Plan-Kala-azar1-Feb2017_light.pdf. Access date: April 17, 2018
Muniaraj M. With little hope of eradicating kala-azar (visceral leishmaniasis) by 2010, outbreaks of which occur periodically in India, should vector control measures or human immunodeficiency virus coinfection or treatment be blamed? Topparasitol. 2014;4:10-9.
Thakur K.P. New strategy to eradicate kala azar in rural Bihar. Indian Journal of Medical Research. 2007;126:447–51.
Post time: May-20-2024