Impact of seasonality and malaria control interventions on Anopheles density and species composition from three areas of Uganda with differing malaria endemicity

This study was conducted from October 2011 to June 2016 in three sites with differing malaria endemicity, within Walukuba, Kihihi and Nagongera sub-counties (Fig. 1), as part of the PRISM1 (Programme for Resistance, Immunology, Surveillance and Modelling of Malaria) project [10, 44, 45] [46]. Walukuba sub-county (00°26′33.2″N, 33°13′32.3″E), located on the fringes of Lake Victoria in Jinja District, eastern Uganda is a peri-urban area at an elevation of 1,215 m with low malaria transmission [baseline annual human biting rate of 537 and P. falciparum entomological inoculation rate (EIR) of 3.2 infective bites per person per year] [46, 47]. Anopheles arabiensis has been the predominant malaria vector species in this area [46, 48]. Kihihi sub-county (00°45′03.1″S, 29°42′03.6″E), located in Kanungu District, southwestern Uganda, is a rural and hilly area 1,310 m above sea level, with moderate malaria transmission (baseline annual human biting rate of 1,337 and P. falciparum EIR of 14.2 infective bites per person per year) [46]. Anopheles gambiae s.s. has been the main malaria vector species in Kihihi [46, 48]. Nagongera sub-county (00°46′10.6″ N, 34°01′34.1″ E), located in Tororo District, eastern Uganda, is a rural area bordering Kenya with an elevation of 1,185 m with high malaria transmission (baseline annual human biting rate of 16,606 reported in 2014 and P. falciparum EIR of 310 infective bites per person per year) [46]. Anopheles gambiae s.s. has been described as the main malaria vector in Tororo [48], however, in 2014 increasing proportions of An. arabiensis were documented [46]. Seasonality in Uganda is characterized by alternating rainy and dry seasons and a bimodal rainfall pattern. The longer rainy season occurs between July and November and the shorter rainy season between February and May [33].

During 2011–2016, the primary malaria control interventions deployed in Uganda included artemisinin-based combination therapy for treatment of uncomplicated malaria, distribution of LLINs through mass campaigns, and IRS in select districts [5]. LLINs were delivered to Walukuba and Nagongera in November 2013, and to Kihihi in June 2014. In Nagongera, three rounds of IRS with a carbamate insecticide (bendiocarb) were implemented between December 2014 and June 2015 (1st: December 2014 to Feb 2015, 2nd: June-July 2015, and 3^rd: November–December 2015).

During the initial enrollment period in 2011, 100 households per site were randomly selected from a list of enumerated of households, as previously described [44]. In 2013, additional households were enrolled to replace households that had dropped out of the study to increase the number of enrolled households back to 100 per site (Fig. 2).

Mosquitoes were collected monthly from cohort study households using miniature CDC light traps (Model 512; John W. Hock Company, Gainesville, FL, USA) set at 19:00 h and collected the following morning at 07:00 h. One trap was set per household each month from October 2011 to June 2016. Light traps were positioned indoors, 1 m above the ground at the foot end of the bed, next to a study participant, sleeping under a LLIN [46]. Data were excluded from analysis if the target occupant did not sleep in the selected room or if the light trap was faulty.

All anophelines collected were scored morphologically under dissecting microscopes at the study sites using taxonomic keys [21, 49]. A subset of 30–50 mosquitoes was randomly selected per month per site for the entire study period for purposes of identifying members of the An. gambiae species complex using PCR [50]. The An. funestus species complex was not processed beyond morphological identification due to resource limitations (henceforth referred to as An. funestus). Results from the species identification were extrapolated to the total dataset to establish the species composition of all Anopheles collected at each site every month. Approximately, 10% of the Anopheles collected were non-malaria transmitting Anopheles christyi, classified as ‘other Anopheles species’ and were not processed further.

Field entomologists recorded CDC light trap data on standardized forms. The data collection forms were double-entered into a Microsoft Access database and checked for discrepancies. Any subsequent inconsistencies were resolved using original data entry forms. Statistical analysis was done using Stata (version 14.2, Stata Corp, College Station, TX, USA).

The primary independent variables investigated were; seasonality (dry versus wet season) and the combined vector control interventions (pre-intervention versus post-intervention). The outcomes of interest were vector density and species composition. Seasonality, denoted by rainy and dry seasons was generated for each site independently. For each site, the same consecutive months were divided into 2 rainy seasons and 2 dry seasons over 1 calendar year. Months with rainfall above and below the median value for the entire observation period were classified as rainy or dry season, respectively, after including a 1-month lag period. Vector density was determined by the number of mosquitoes collected per household per month per site and stratified by seasonality and the period before intervention implementation versus the period after intervention implementation. Simple proportions were compared using a log-binomial regression model with generalized estimating equations to adjust for repeated measures from the same house.

Here, we expand on the PRISM1 results previously reported by Kilama et al. [46] from observations carried out over 12 months (October 2011 to September 2012), by describing species-specific changes in response to vector control interventions carried out over 57 months (October 2011 to June 2016). Musiime et al. also used PRISM1 data to examine the impact of vector control interventions on Anopheles mosquito composition in Nagongera only, as measured using indoor and outdoor human landing catches [10]. This study analyses mosquitoes collected indoors using CDC light traps using longitudinal sampling in the three study sites. The PRISM1 dataset can be accessed at https://​clinepidb.​org/​ce/​app/​record/​dataset/​DS_​0ad509829e.

In each study site, the head of household or adult representative was approached for consenting before household recruitment. A written informed consent was obtained as permission to conduct CDC light trap collections within the household. The study was approved by the Uganda National Council for Science and Technology (HS-119ES), Makerere University School of Medicine Research and Ethics Committee (2017-099), the University of California, San Francisco Committee on Human Research (17-22544) and London School of Hygiene and Tropical Medicine Ethics Comittee (14266-6).

From October 2011 to June 2016, 16,002 light trap collections were performed monthly across the three study sites. Overall, 158,095 Anopheles mosquitoes were collected, including 4,640 (3%) from Walukuba, 18,474 (12%) from Kihihi, and 134,981 (85%) from Nagongera (Table 1, Fig. 2). The number of Anopheles mosquitoes collected per household per night (vector density) varied across the sites from 0.89 in Walukuba to 25.11 in Nagongera (Table 1). Overall, An. arabiensis (n = 2,391) was the predominant malaria vector species in Walukuba accounting for 52% of all collections. In Kihihi, nearly all Anopheles collected (98%) were An. gambiae s.s. (n = 18,135), while in Nagongera, 65% were An. gambiae s.s. (n = 87,936) (Table 1). Of the 1,413 ‘other’ Anopheles species collected in the sites, 1,385 (98%) were identified morphologically as An. christyi, which is classified as a non-malaria vector [51]. There is historical evidence that An. christyi has the ability to transmit malaria parasites [52], however, subsequent reports argue that this ability was either lost or suppressed independently [51] and is thus now considered to be a non-malaria vector. As expected, more Anopheles mosquitoes were collected during rainy seasons, compared to the dry seasons (Table 2).

In Walukuba, the rainy season was associated with approximately a three-fold increase in vector density for all three main vectors, including An. gambiae s.s. (density ratio [DR] 3.21, 95% confidence interval [CI]: 2.15–4.79), An. arabiensis (DR 2.84, 95% CI: 1.87–4.32) and An. funestus (DR 2.57, 95% CI: 1.36–4.88; Table 2). Following LLIN distribution, approximately a threefold decline in An. funestus vector density (DR 0.34, 95% CI: 0.18–0.65; Table 2) was observed in Walukuba. The density of An. gambiae s.s. or An. arabiensis following distribution of LLINs was similar to levels before deployment (Table 2). This corresponded with the pattern of distribution observed in the graphical plots examining the absolute numbers of Anopheles collected in Walukuba (Fig. 3a) and the relative proportions (Fig. 4a) of mosquito species.

In Kihihi, the rainy season was associated with over a five-fold increase in An. gambiae s.s. density (DR 5.56, 95% CI: 3.90–7.92) compared to the dry season. Insufficient numbers of both An. arabiensis and An. funestus were collected however, precluding further analysis. LLIN distribution in this area was associated with a decrease in A. gambiae s.s. vector density (DR 0.68, 95% CI: 0.49–0.94). This observation is supported by the longitudinal patterns for absolute numbers of Anopheles mosquitoes collected per household (Fig. 3b). When focusing only on trends in relative proportions of Anopheles over time, however, this finding is not obvious (Fig. 4b).

In Nagongera, there were substantially more An. gambiae s.s. (DR 12.2, 95% CI: 7.05–21.3) and An. arabiensis (DR 7.75, 95% CI 4.21–14.3) during the rainy season, but no significant difference was observed for An. funestus (DR 1.61, 95% CI: 0.97–2.66). LLINs were associated with a significant decrease in vector density for An. gambiae s.s. (DR 0.40, 95% CI: 0.21–0.73) and An. arabiensis (DR 0.36, 95% CI 0.18–0.72), but not An. funestus (DR 0.61, 95% CI: 0.36–1.04). In Nagongera, three rounds of IRS with bendiocarb were delivered following LLIN distribution. The first round of IRS was associated with a 20-fold decline in An. gambiae s.s. vector density compared to the pre-LLIN period (DR 0.05, 95% CI: 0.02–0.16), while the impact on An. funestus was close to elimination (DR 0.02, 95% CI: 0.008–0.06). There was no difference in An. arabiensis densities before and after the first round of IRS (DR 0.33, 95% CI: 0.10–1.09). The 2nd and 3rd rounds of IRS (combined) were associated with further declines in vector density for both An. gambiae s.s. (DR 0.004, 95% CI: 0.002–0.009), and An. funestus (DR 0.001, 95% CI: 0.0005–0.004), but a less pronounced decline was observed in An. arabiensis vector density (DR 0.15, 95% CI: 0.07–0.33). In contrast to Walukuba and Kihihi, substantial reductions in the absolute numbers of An. gambiae s.s. and An. funestus s.l. were observed following the addition of IRS to LLINs (Fig. 3c). The absolute number of An. arabiensis changed less after the introduction of the mass vector control measures, and, as a result, the relative proportion of An. arabiensis increased markedly as the populations of An. gambiae s.s. and An. funestus collapsed, with An. arabiensis left as the predominant species after IRS (Fig. 4c).