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  • Open Access

Dramatic decreases of malaria transmission intensities in Ifakara, south-eastern Tanzania since early 2000s

  • 1, 2Email author,
  • 1,
  • 1,
  • 1, 3, 4,
  • 1,
  • 1, 2 and
  • 1, 2, 5
Malaria Journal201817:362

https://doi.org/10.1186/s12936-018-2511-2

  • Received: 5 July 2018
  • Accepted: 8 October 2018
  • Published:

Abstract

Background

Ongoing epidemiological transitions across Africa are particularly evident in fast-growing towns, such as Ifakara in the Kilombero valley, south-eastern Tanzania. This town and its environs (population ~ 70,000) historically experienced moderate to high malaria transmission, mediated mostly by Anopheles gambiae and Anopheles funestus. In early 2000s, malaria transmission [Plasmodium falciparum entomological inoculation rate (PfEIR)] was estimated at ~ 30 infectious bites/person/year (ib/p/yr). This study assessed the PfEIR after 15 years, during which there had been rapid urbanization and expanded use of insecticide-treated nets (ITNs).

Methods

Randomly-selected 110 households were sampled across Ifakara town and four adjacent wards. Mosquitoes were trapped nightly or monthly (June.2015–May.2016) using CDC-light-traps indoors, Suna® traps outdoors and human landing catches (HLC) indoors and outdoors. All Anopheles mosquitoes were morphologically identified and analysed by ELISA for Plasmodium circumsporozoite proteins. Mosquito blood meals were identified using ELISA, and sub-samples of An. gambiae and An. funestus examined by PCR to distinguish morphologically-similar siblings. Insecticide resistance was assessed using WHO-susceptibility assays, and some Anopheles were dissected to examine ovariole tracheoles for parity.

Results

After 3572 trap-nights, one Plasmodium-infected Anopheles was found (an An. funestus caught outdoors in Katindiuka-ward by HLC), resulting in overall PfEIR of 0.102 ib/p/yr. Nearly 80% of malaria vectors were from Katindiuka and Mlabani wards. Anopheles gambiae densities were higher outdoors (64%) than indoors (36%), but no such difference was observed for An. funestus. All An. funestus and 75% of An. gambiae dissected were parous. Anopheles gambiae complex consisted entirely of Anopheles arabiensis, while An. funestus included 84.2% An. funestus s.s., 4.5% Anopheles rivulorum, 1.4% Anopheles leesoni and 9.9% with unamplified-DNA. Anopheles gambiae were susceptible to bendiocarb and malathion, but resistant to pyrethroids, DDT and pirimiphos-methyl. Most houses had brick walls and/or iron roofs (> 90%), and 52% had screened windows.

Conclusion

Malaria transmission in Ifakara has decreased by > 99% since early-2000s, reaching levels nearly undetectable with current entomological methods. These declines are likely associated with ITNs use, urbanization and improved housing. Remaining risk is now mostly in peri-urban wards, but concerted efforts could further decrease local transmission. Parasitological surveys are required to assess actual prevalence, incidence and importation rates.

Background

Globally, human populations and settlements are undergoing rapid expansion, concurrent with speedy urbanization [1]. In the 1950s, less than one-third of global population lived in urban areas, but this had increased to 54% by 2014 and will likely exceed two-thirds by 2050 [1]. Most of these changes are expected to occur in developing countries in Africa and Asia [1, 2]. In Tanzania, about 33% of people currently live in urban areas, compared to just 20% in 2000 [3, 4].

Malaria burden has significantly declined worldwide over the past two decades, largely because of deliberate control efforts like scale-up of insecticide-treated nets (ITNs) and improved treatments [5] as well as due to socio-economic developments [6, 7]. Urbanization is one of the major aspects of development that significantly impacts malaria transmission [79]. Processes of urbanization reduce malaria transmission primarily because urban environments lack suitable Anopheles breeding habitats [810]. Additionally, urban settings generally have better access of health services and improved housing as well as greater education and awareness than rural areas [7, 9]. In a 2013 analysis, Tatem et al. concluded that ongoing urbanization in endemic regions would cause further declines in malaria transmission, and that these declines would be synergized by the deliberate scale-up of core malaria control tools [7].

Ifakara town, in the Kilombero river valley in the mostly agricultural Morogoro region, is among the fast growing towns in Tanzania. The town is surrounded by rain forests, wetlands and vast savannah lands, making it an attractive setting for both farmers and pastoralists. It is also a key trading hub in the region, mostly for farm and animal products. Towns such as Ifakara that appear in the middle of low-lying tropical floodplains present unique scenarios for assessing spatial and temporal variations of malaria transmission intensities [i.e. entomological inoculation rates (EIR), measured as number of infectious bites/person/year (ib/p/yr)]. Historical measures of EIR in Ifakara date back to 1965, when the town still had just one-fifth of its current population, and when Freyvogel and Kihaule conducted a small survey of Anopheles mosquitoes in the area [11]. Though parasite assessment techniques at the time were still fairly insensitive compared to options available today, Freyvogel and Kihaule reported monthly “apparent inoculation rates” for both Anopheles gambiae and Anopheles funestus, the two predominant malaria vectors at the time [11]. The “apparent inoculation rate” represented how frequently a single person was likely to get infected, and was estimated based on factors such as proportion of mosquitoes with sporozoites in their salivary glands upon dissection, gonotrophic cycles of the mosquitoes, and estimated daily biting rates (computed as ratio of total indoor mosquito catches and number of sleepers in collection room). In later years, there were multiple EIR assessments in the surrounding villages, including which reported 329 ib/p/yr from early 1990s in Namawala, a rural community 30 km away from the Ifakara town [12]. Because of the differences in assessment methods, it is difficult to directly compare these historical estimates against measures obtained after 1990s onwards.

In 2003, Drakeley et al. published findings of another entomological survey that assessed malaria transmission intensities in Ifakara, which at that time was still a peri-urban area surrounded by very high transmission villages in the Kilombero valley [13]. In the years preceding this report, the team had conducted fortnightly mosquito collections using CDC-light traps inside 32 randomly selected houses in Ifakara area. They used two different methods to estimate the PfEIR. First was the standard method, which involved calculations of the product of sporozoites rate (SR) and human-biting rate (BR), and the second method involved calculation of the ratio of sporozoites-positive mosquitoes to the number of nightly catches. Drakeley et al. estimated an EIR of 31 ib/p/yr using the standard method and 29 ib/p/yr using the alternative method [13].

This study aimed to assess malaria transmission intensities in Ifakara town and its adjacent wards, more than a decade after the assessment by Drakeley and colleagues. This new study employs similar mosquito sampling techniques, but with addition of indoor/outdoor comparisons of transmission risk, and an inclusion of an odour-bated trap to sample outdoor host-seeking vectors. Similar methods were also used for calculating and reporting PfEIR as used by Drakeley et al. [13]. The primary objective was to assess the extent to which malaria transmission in the study area had changed over the past 15 years, considering the rapid growth in population [2, 14], urbanization trends [15] and the sustained use of effective malaria treatment as well as prevention measures such as ITNs [1619].

Methods

Study site

This study was conducted in Ifakara town and its adjacent wards located in the Kilombero river valley, in south-eastern Tanzania (Fig. 1). The small but rapidly-growing town lies at 8.1336° South and 36.855° East. It has an average altitude of 270 m above sea level, annual rainfall of 1200–1800 mm, annual temperature range between 20 and 32 °C, and relative humidity between 51 and 71%. The Ifakara town area is administratively divided into five wards, namely Ifakara Mjini (Ifakara town centre), Katindiuka, Lipangalala, Mlabani and Viwanja Sitini. In 2012, when the last national census was conducted, Ifakara town had a population of 55,956 people [20]. Given the estimated country population growth rate of 3.1%, the town currently has an estimated population of about 67,345 people as of 2018 [2, 3]. In 2002 when Drakeley and colleagues had conducted the initial survey, Ifakara town had a population of 45,518 people [20], and between then and now the population has increased by nearly 50%. Ifakara Mjini and Viwanja Sitini, located on the western part of the town, are the most urban of the five wards, and the inhabitants are largely small business owners. These two wards make up of about 56% of the total population. On the other hand, Katindiuka and Mlabani, located on the eastern part of the town, are the most rural and least densely populated of the five wards and are surrounded by seasonal rice farms. This study was conducted in all the five wards (Fig. 1) for a period of 12 months between June 2015 and May 2016.
Fig. 1
Fig. 1

Map of the study areas showing five wards that make up the Ifakara town

Recent studies conducted in neighboring villages in the valley have shown that primary malaria vector species include Anopheles arabiensis and An. funestus, with the latter mediating > 80% of the ongoing transmission [21]. Other Anopheles mosquitoes found in the area are Anopheles coustani, Anopheles pharoensis, Anopheles squamosus, Anopheles ziemanni, Anopheles maculipalpis and Anopheles wellcomei. Malaria vector densities are highest during the rainy season, with peaks between March and May, though An. funestus densities may peak after the rains [22]. The major vector control intervention is long-lasting insecticide-treated nets (LLINs), mostly provided freely by the Government; the last such mass-distribution of the nets having been in 2016.

Sampling procedures

Geo-referenced house-hold database obtained from Ifakara Health and Demographic Surveillance Systems (IHDSS) [23] covering the five wards was used to select the households for this study. A total of 22 households were randomly selected from each of the five wards, ensuring approximately equal representation from each Balozi (an administrative cluster consisting of 10–100 households). In each ward, two households were visited for 5 days every month for 12 months, and mosquito sampling was done both indoors and outdoors. The other 20 households were each visited twice every month, once for indoors and once for outdoors mosquito sampling. Altogether a total of 3600 trap-nights were planned in 110 households in the five wards for a period of 12 months between June 2015 and May 2016. The full sampling plan is shown in Table 1.
Table 1

Illustration of complete sampling plan for the vector surveillance and estimation of malaria transmission in Ifakara town and its environs

Trap type

Position

Sample station

Frequency

Number of houses

Number of trap nights

Human landing catches

Indoors

Sentinel

Hourly collections, 5 nights per month

10

600

Human landing catches

Outdoors

Sentinel

Hourly collections, 5 nights per month

10

600

CDC light traps

Indoors

Random

Nightly collections, 1 night per month

100

1200

Suna® Traps

Outdoors

Random

Nightly collections, 1 night per month

100

1200

The table shows the different trapping methods used, their frequency of use, position of use (indoors/outdoors), and whether the methods were used in sentinel or randomly selected households. The sampling plan does not include the additional collections done to assess parity

Mosquito collections indoors and outdoors

Indoor mosquito catches were done using standard Centres for Disease Control (CDC) light traps [24], set near human-occupied bed nets (Fig. 2). The light traps were suspended at the foot-end of the bed, approximately 1.5 m above ground [25]. The traps were set between 18:00 h and 06:00 h each sampling night in a room with at least one person sleeping under an intact bed net. Where no intact nets were available in the sampling room, one was provided freely to each consenting household. The traps were retrieved each morning starting 06:00 h; the field team taking approximately 30 min to collect all traps from all household stations. Outdoor mosquito collections were done using odour-baited Suna® Traps [26] (Fig. 2). The Suna® traps were suspended 30 cm above the ground, and 5–10 m from the selected house. The traps were baited using a combination of Ifakara synthetic lure [27] dispensed in pellets and CO2 gas produced from yeast-molasses fermentation [28]. Like the CDC light traps, the Suna® traps were set between 18:00 and 06:00 h and retrieved at 06:00 h each subsequent morning. To minimize any direct competition between the indoor and outdoor traps, the sampling plan was such that the Suna® and CDC light traps where never at the same house at any night, instead being placed in different nights. This specific dataset was, therefore, also not used for comparing indoor vs. outdoor biting rates. These CDC light trap and Suna™ trap collections were done consistently in the 20 selected houses in each ward, totaling 100 households, each sampled monthly for indoors collections and monthly for outdoor collections.
Fig. 2
Fig. 2

Pictures of sampling methods used: a SUNA trap placed outdoors and b CDC-light trap placed near occupied bed net

To assess and compare indoor and outdoor biting preferences and nightly biting patterns, additional collections were done in 10 households using human landing catches method, working with consenting and trained adult male volunteers. In each of the 10 households, two adult male volunteers sat either indoors or outdoors and collected mosquitoes hourly, working for 45 min and resting for 15 min every hour. The collections were done between 18:00 and 06:00 h and each household was sampled for five nights every month for 12 months. In the 5-day period of HLC, the collections were done every other day, to allow volunteers a day of resting between the collections. All collected mosquitoes were kept in paper-cups and labeled to show date, hour of collection and household identification codes.

At the end of this survey, additional adult malaria vectors [An. gambiae sensu lato (s.l.) and An. funestus] were collected using CDC light traps, and used to assess parity rates and estimate physiological age of the mosquito populations in the area [29]. These additional collections were done for 1 month in 10 households per ward in three of the five study wards, i.e. Katindiuka, Mlabani, and Lipangalala.

Sorting and laboratory analysis of mosquito samples

Each morning, mosquitoes collected the previous night were killed using 70% ethanol and sorted by sex and taxa using morphological identification keys, primarily to separate Anopheles mosquitoes from other genera, and to identify major malaria vector groups [30, 31]. Abdominal status of the female Anopheles mosquitoes was assessed and recorded as unfed, blood-fed or gravid. Anopheles mosquito samples were packed in labeled plastic micro-centrifuge tubes (Eppendorf®) with silica gel as desiccant and sent to the Ifakara Health Institute laboratory for further analyses, including: (a) species identification using Polymerase Chain Reaction (PCR) [32, 33], (b) detection of Plasmodium parasites in primary malaria vectors using Enzyme Linked Immunosorbent Assays (ELISA) [34], and (c) identification of mosquito blood meals using ELISA [35]. The specific methods used for the laboratory analyses were as follows:

PCR assays for species identification

DNA was first extracted from the mosquito hind legs. For An. gambiae s.l., PCR amplification was done for the species-specific nucleotide sequences of the ribosomal DNA (rDNA) intergenic spacer regions (IGS) in a 25 µl reaction volume of PCR mixture following methods first described by Scott et al. [33]. For An. funestus group mosquitoes, methods developed by Koekemoer et al. [32] were used to distinguish between up to five members of the An. funestus group, including the three that have been previously described in villages around the current study area, i.e. An. rivulorum, An. funestus sensu stricto (s.s.) and An. leesoni [21]. Here, amplification was done for the species-specific non-coding regions of the internally transcribed spacer 2 (ITS2) region on the rDNA. After amplification, the post-PCR amplicons for both the An. gambiae and An. funestus were analysed by electrophoresis in agarose gel stained with ethidium bromide. Visible DNA bands were photographed under ultraviolet light using Kodak Gel Logic 100 imaging system.

ELISA assays for parasite detection in mosquito salivary glands

To detect P. falciparum circumsporozoite proteins (CSP) in the salivary glands of the mosquitoes, ELISA assays were done on pooled samples separately for An. gambiae s.l. and An. funestus. Sample pooling was done by household ID, hour and date of collection and sampling method, and did not exceed 10 individual mosquitoes. Where fewer than 10 mosquitoes of one species complex were collected by a given method on a given hour or date at a given house, then the pool size was also less than 10. The optical density of post-ELISA lysate was measured at 405–414 nm after 45 min using ELISA plate readers. ELISA lysates for all postive samples were boiled at 100 °C for 15 min, to eliminate any false positives, usually associated with heat-labile protozoans in Anopheles mosquitoes, especially those with previous bovine blood-meals [3638].

ELISA assays for identification of mosquito blood meal sources

Abdomens of all the blood-fed mosquitoes were screened to detect antigens for human, bovine, goat, dog and chicken blood antigens, as these are the most likely blood-meal sources in the area. Anti-IgG antibodies from each host were used to detect host antigen in the blood meal of the mosquito based on the method described by Beier et al. [35].

Assessment of insecticide susceptibility rates in malaria vectors

The World Health Organization (WHO) standard insecticide susceptibility assays [39] were performed on female adult mosquitoes raised from larvae sampled from two of the five study wards, i.e. Katindiuka and Viwanja Sitini. These assays were possible only with An. arabiensis, but not An. funestus because of the low densities and cryptic nature of An. funestus aquatic habitats in the area. Mosquito larvae were reared to adult stage under standard insectary conditions (temperature of 27 ± 2 °C and relative humidity of 70–90%) inside the Ifakara Health Institute mosquito laboratory, the VectorSphere. The larvae could feed on the mud and some algae brought together during collection but small quantities of Tetramin® baby fish (Tetra, Melle, Germany) food was also added. Each morning pupae were collected and placed in plastic cups inside netting cages measuring 30 m × 30 m × 30 m. Upon emerging, adult mosquitoes were fed on 10% glucose solution prior to exposure to insecticides. Non-blood fed females of An. arabiensis (2–5 days old) were used in the assays, following the WHO test procedures [39]. The bioassays were done on 0.05% lambda cyhalothrin, 0.05% deltamethrin, 0.75% permethrin, 4% DDT, 0.1% bendiocarb, 0.25% pirimiphos-methyl and 5% malathion. Ifakara strain of An. gambiae s.s. was used as a reference susceptible strain. Four replicates of the exposure were done per insecticide, and each exposure used 25 mosquitoes. Mortality was recorded 24 h post-exposure.

Assessment of parity status

Additional adult malaria vectors were collected from inside the houses using miniature CDC light traps [24] to determine their physiological age [29]. These mosquitoes were collected from 10 households in three of the five wards: Katindiuka, Mlabani, and Lipangalala. Each morning, the mosquitoes were killed by refrigeration and immediately dissected under a stereo light microscope. The dissected ovaries were observed on 10× magnification under a compound microscope, to determine parity status by recording presence of stretched ovariole tracheoles (parous females) or coiled tracheolar skeins (nulliparous females), as detailed in the WHO Practical Manual for Entomology [29].

Household characterization

Additional information on the type of construction of each of the 110 houses was collected. This included type of construction material used for the roof and walls, materials used to cover the windows and doors, types of animals the houses kept and whether the houses had access to electricity.

Data processing, analyses and estimation of transmission intensities

Entomological data was recorded in standardized data forms used at Ifakara Health Institute. The data was quality-checked and double-entered into a Microsoft Excel database by two data entry clerks. The two data sets were compared to correct any errors, after which a single clean dataset was produced for further analysis.

Generalized linear mixed models for mosquito catch data

Data analysis was done using R statistical software version 3.3.2 [40]. Generalized linear mixed models (GLMM) [41] were used in analysis of mosquito densities by different study wards, mosquito trapping methods and position, i.e. indoors versus outdoors in cases where HLC had been used. In all analysis, study wards were treated as fixed factor while experimental day was treated as random term, controlling for seasonal variations. Initially, all the models were fitted using Poisson distribution models for count data with log-link functions, but due to poor-fitting and over dispersion, negative binomial distributions were fitted using glmmadmb (generalized linear mixed model automatic differentiation model builder) package [42], which allowed assessment of the zero inflations in the count data. Model testing was done using Akaike Information Criterion (AIC), by sequentially selecting models with lower AIC values. Graphs were produced using a ggplot2 package in R [43]. The mosquitoes caught using HLC were also analysed for nightly biting patterns and graphically presented to illustrate time of night when peak biting occurred.

Calculating PfEIR

Similar approaches as used by Drakeley et al. [13] were applied to calculate Plasmodium infection rates (sporozoites rates) and PfEIR. These analyses were initially done for each administrative ward, each month and each trap type, but was later pooled so that there was a single estimate of infection rate in all mosquitoes collected. Human-biting rates (HBR) were measured directly from human landing collections made indoors. The P. falciparum sporozoites rate (PfSR) was calculated by dividing the number of Plasmodium-positive mosquitoes by the total number of mosquitoes examined by ELISA, and this was expressed as a percentage. Plasmodium falciparum entomological inoculation rate (PfEIR) was then calculated considering the two methods previously used by Drakeley et al. [13] as follows:

The first was the standard method of calculating PfEIR as a product of sporozoites rates and human-biting rates; i.e., C*(Number of sporozoites positive mosquitoes/Number of mosquitoes tested) × (Number of mosquitoes collected/Number of trap nights) × 365 [44], where, C is the relative efficiency coefficient for the relationship between CDC-light trap catches and human landing catches. The second method involved calculation of the ratio of sporozoites positive mosquitoes and number of nightly catches; i.e., C*(no of sporozoites positive mosquitoes/number of trap nights) × 365 [13]. In this study all collected An. gambiae and An. funestus mosquitoes collected were analysed, therefore minimal variation in the two PfEIR estimates were expected. Although multiple trap types were used, PfEIR was only detectable using HLC, since the only sporozoite positive mosquito was collected using this method.

In the analyses by Drakeley et al., they had assumed that traps with extraordinarily large numbers of catches compared to the average, would be consisting mostly of young nulliparous mosquitoes, which are naturally sporozoites negative [45]. Such trap collections were therefore all considered “tested” but with negative CSP result. In this study however, mosquito samples were pooled in maximum pool size of 10 mosquitoes per assay, and all collected samples were analysed so that any biases associated with the said assumptions were effectively eliminated. In the Drakeley et al. analysis [13], a value of 1.605 (equivalent to 0.62 relative efficacy of CDC-light traps to HLC) was used based on previous studies by Charlwood et al. [45]. However, in recent estimates by Kaindoa et al. [21], values of 0.3 for An. funestus and 0.68 for An. gambiae s.l. were used based on 2004 estimates by Okumu et al. [46] for the relative efficiencies of CDC-light trap catches relative to HLC. However, in a wide-ranging review of the estimates of CDC light trap and HLC catches across Africa, Briët et al. [47] concluded that there is no need to do these conversions, and that the CDC-Light trap estimate may be used as is. In this study therefore, PfEIR estimates were calculated without adjustments. The coefficient, C, in the PfEIR equations was fixed at 1, assuming all the trap types had similar efficiency across space and time. Nonetheless, any adjustments in human biting rates would be insignificant given no sporozoites positive mosquitoes were collected by any method other than HLC.

Assessing mosquito host preferences

Contributions of different blood meal sources in the malaria vectors were estimated as percentages of total mosquitoes assayed.

Analysis of spatial patterns in vector abundance

Mosquito densities and distribution across the study area was assessed for evidence of spatial associations and clustering. Spatial patterns of total nightly mosquito densities per house were examined using ArcGIS 10.4 spatial analyst tool (ESRI, USA). The Getis-Ord Gi* statistic [48, 49] in Arc-GIS was used to identify households with significant clustering of high densities of the two main malaria vectors; An. arabiensis and An. funestus. Clusters depicting both the high vector density foci and low-vector density foci were identified and their statistical significance determined at Gi* P value ≤ 0.05, and Gi* Z score ≥ 1.96. Spatial relationships between households were assumed to be inversely related, such that houses far apart were more likely to differ in vector densities than households near one another. The analysis was done assuming Euclidean distances between neighboring mosquito sampling stations [48, 49].

Results

Overall mosquito densities

A total of 264,012 mosquitoes were collected in the 3572 trap nights, considering all trap types. The collections comprised of 257,399 culicines (97.5%) and 8613 Anopheles mosquitoes (3.3%). The Anopheles included 7795 An. arabiensis (90.5%), 400 An. funestus (4.6%), 310 An. coustani (3.6%) and 112 of other Anopheles species (1.3%).

Indoor and outdoor densities of the mosquitoes were compared using the HLC method. Overall, An. gambiae densities were higher outdoors (64%) than indoors (36%), while only marginal differences were observed in An. funestus, 46% of which were indoors and 54% outdoors (Table 2). Similar patterns were observed across the five wards (Fig. 3). The nightly biting patterns of mosquitoes collected by HLC in the ten sentinel houses in each ward are also shown in Fig. 4, which also shows that indoor collections were significantly lower than outdoor collections for An. arabiensis but not An. funestus throughout the night.
Table 2

Number of mosquitoes of different species collected indoors and outdoors by the different trapping methods in both sentinel and random stations across the study area

Trapping method

No. trap nights

No. houses

Mosquito species

No. mosquitoes indoors (%)

No. mosquitoes outdoors (%)

Total

Human landing catches

1200

10

Anopheles arabiensis

1807 (36.2%)

3187 (63.8%)

4994

Anopheles funestus

94 (46.3%)

109 (53.7%)

203

Other Anopheles species

32 (9.5%)

305 (90.5%)

337

Mansonia spp.

461 (30.6%)

1048 (69.4%)

1509

Culex spp.

68,582 (54.3%)

57,716 (45.7%)

126,298

CDC-light traps

1192

100

Anopheles arabiensis

1736 (100%)

NA

1736

Anopheles funestus

158 (100%)

NA

158

Other Anopheles species

27 (100%)

NA

27

Mansonia spp.

832 (100%)

NA

832

Culex spp.

51,958 (100%)

NA

51,958

Suna® traps

1180

100

Anopheles arabiensis

NA

1065 (100%)

1065

Anopheles funestus

NA

39 (100%)

39

Other Anopheles species

NA

54 (100%)

54

Mansonia spp.

NA

1551 (100%)

1551

Culex spp.

NA

73,251 (100%)

73,251

Fig. 3
Fig. 3

Proportions of indoor and outdoor mosquito catches in the sampling stations for; a An. arabiensis; b An. funestus. This figure illustrates only data collected using human landing catches

Fig. 4
Fig. 4

Distribution of mosquitoes in Ifakara town: a hourly distribution of An. funestus; b hourly distribution of An. arabiensis; c monthly distribution of An. funestus and d monthly distribution of An. arabiensis

Spatial and temporal variations of malaria vector biting risk

Over the entire sampling period, 78.5% of all the primary malaria vectors were collected from two of the five wards, i.e. Katindiuka and Mlabani, which were the peri-urban parts. The remaining 21.5% were collected from the rest of the study area, i.e. Ifakara Mjini, Lipangalala and Viwanja Sitini, which were the more urbanized parts. In particular, Katindiuka ward, with houses constructed very close to mosquito breeding habitats (Fig. 5) had the higher densities of both An. arabiensis and An. funestus compared to the other wards (Table 3). Within Katindiuka ward itself, highest densities were in households closer to the adjacent rice fields on the eastern side of the ward, compared to households at the centre and on the western side (Fig. 6). This distribution pattern was observed for both An. arabiensis and An. funestus, although An. funestus were caught at a much lower densities (Table 3). Densities of An. arabiensis were highest between January and May, peaking in February, while the An. funestus densities were highest between May and August, peaking in June (Fig. 4).
Fig. 5
Fig. 5

Examples of house structures and locations in Ifakara town: a a typical house in Katindiuka ward, showing aquatic breeding habitats of Anopheles mosquitoes (rice fields and water ponds) nearby and b a typical house in the more urban settings of Ifakara town

Table 3

Mean numbers of mosquitoes of different species caught each night in the four adjacent wards of Ifakara, relative to the numbers caught in Ifakara Mjini (the main town centre)

Species

Wards

Mosquito catches indoors

Mosquito catches outdoors

Total no. mosquitoes

Adjusted means [UC–LC]

RR [UC–LC]

P-values

Total no. mosquitoes

Adjusted means [UC–LC]

RR [UC–LC]

P-values

Anopheles arabiensis

Ifakara Mjini

154

0.42 [0.25–0.70]

1

N/A

269

0.29 [0.17–0.51]

1

N/A

Katindiuka

2337

5.81 [3.53–9.56]

13.8 [6.9–27.6]

< 0.001

2318

3.28 [1.98–5.42]

11.2 [5.33–23.7]

< 0.001

Lipangalala

254

0.16 [0.09–0.30]

0.4 [0.2–0.8]

< 0.05

531

0.10 [0.05–0.19]

0.3 [0.14–0.79]

< 0.05

Mlabani

580

0.22 [0.12–0.39]

0.5 [0.2–1.1]

0.084

939

0.31 [0.18–0.53]

1.1 [0.48–2.29]

0.898

Viwanja Sitini

225

0.64 [0.38–1.07]

1.5 [0.7–3.1]

0.257

188

0.54 [0.32–0.92]

1.9 [0.87–4.00]

0.11

Anopheles funestus

Ifakara Mjini

7

0.002 [0–0.02]

1

N/A

10

0.02 [0.01–0.05]

1

N/A

Katindiuka

189

0.49 [0.25–0.97]

212.7a [24.1–1881]

< 0.001

116

0.10 [0.05–0.18]

4.5 [1.68–14.78]

< 0.001

Lipangalala

35

0.03 [0.01–0.10]

14.5a [1.6–132.0]

< 0.05

4

0 [0–0]

0 [0–0]

0.802

Mlabani

16

0.01 [0.003–0.04]

5.0 [0.5–53.9]

0.184

19

0.03 [0.01–0.07]

1.4 [0.39–4.83]

0.627

Viwanja Sitini

1

0.002 [0–0.02]

1.1 [0.1–21.2]

0.942

3

0.01 [0.002–0.03]

0.4 [0.07–2.24]

0.298

The catch densities are also illustrated in maps in Fig. 6

aExcessively dispersed data not tractable by the generalized linear mixed models, mostly because of very low-densities of An. funestus mosquitoes

Fig. 6
Fig. 6

Map showing distribution of a An. arabiensis and b An. funestus within the five wards of Ifakara town. All clusters depicting areas with households where the highest densities are most spatially concentrated were first identified, after which statistically significant ones were determined at level of Gi* P value ≤ 0.05, and Gi* Z score ≥ 1.96. The actual Getis-Ord Gi* statistics are provided to illustrate areas with maximum and minimum vector densities

Sibling species of primary malaria vectors

A total of 7795 An. gambiae s.l. and 400 An. funestus s.l. mosquitoes were assayed in the laboratory by PCR for species ID, which represented all the mosquitoes of this species collected during the entire sampling season. The same mosquitoes were also assessed by ELISA (results shown elsewhere in this paper). All An. gambiae s.l. mosquitoes were confirmed to be An. arabiensis (100%). On the other hand, the An. funestus group consisted of An. funestus s.s. as the dominant sibling species (84.2%), An. rivulorum (4.5%), An. leesoni (1.4%) and a few unamplified samples (9.9%). The unamplified DNA specimen may have been due to improper handling of the specimen prior to PCR, lack of appropriate primers in the PCR assays to detect actual species or incorrect morphological identification before the samples were analysed by PCR.

Mosquito blood meal sources

A total of 74 mosquitoes with blood meals in their abdomen were also assessed by ELISA to identify the source of their meals. These included 38 An. arabiensis and 36 An. funestus s.s. The blood-feeding proportions were relatively higher for An. funestus [i.e. 9% (36 out of 400) were blood-fed] compared to An. arabiensis for which 0.05% (38 out of 7795) were blood fed. Only human blood was identified in all 74 mosquitoes.

Insecticide susceptibility test results

The insecticide susceptibility test results are summarized in Table 4. Based on the WHO guidelines used here [39], the An. arabiensis mosquitoes raised from field-collected larvae were found to be susceptible to the carbamate, bendiocarb and to the organophosphate, malathion, but they were resistant to all the pyrethroids tested, i.e. permethrin, deltamethrin, and lambda-cyhalothrin, as well as the organochloride, DDT. Surprisingly, the mosquitoes were also resistant to the other tested organophosphate, i.e. pirimiphos-methyl. This compound has not been used for vector control in this region but is a common agricultural pesticide, and has been used for IRS in northern Tanzania over past 4 years. These insecticide resistance patterns were observed in both Katindiuka and Viwanja Sitini wards, where the tested mosquitoes were obtained. Full susceptibility (100% mortality) was observed in the susceptible Ifakara strain of An. gambiae s.s., used here as control. Knock down resistance (kdr) assays were not assessed, but a previous study done in villages in the neighboring Ulanga district indicated absence of kdr mutation genes [50].
Table 4

Susceptibility of wild An. arabiensis mosquitoes collected in two wards in the study area, i.e. Katindiuka and Viwanja Sitini, (Fig. 1)

Insecticide tested

Katindiuka ward

Viwanja Sitini ward

N

Mortality (%)

Class

N

Mortality (%)

Class

0.75% permethrin

100

37

Resistant

100

37

Resistant

0.05% deltamethrin

100

39

Resistant

100

36

Resistant

0.05% lambda-cyhalothrin

100

32

Resistant

100

34

Resistant

4% DDT

100

41

Resistant

100

65

Resistant

0.1% bendiocarb

100

100

Susceptible

100

100

Susceptible

0.25% pirimiphos methyl

100

54

Resistant

100

70

Resistant

5% malathion

100

100

Susceptible

100

99

Susceptible

Control (untreated paper)

100

0

N/A

100

0

N/A

The assays were conducted using WHO 2013 guidelines for testing insecticide resistance in malaria vectors [39]

In tests conducted on the susceptible An. gambiae s.s. (Ifakara strain) used as an additional control, full susceptibility (100% mortality) was observed

Parity rates of An. arabiensis and An. funestus mosquitoes

A total of 109 An. arabiensis and 4 An. funestus were dissected to determine their parity status. All of these mosquitoes were collected from Katindiuka, Mlabani and Lipangalala wards. Of these, 74.6% of the An. arabiensis were parous and 25.7% were nulliparous. On the other hand, all four An. funestus were found to be parous (Table 5).
Table 5

Plasmodium infectious status of primary malaria vectors collected indoors and outdoors in Ifakara town and estimates of the entomological inoculation rates, as contributed by the vector species

 

Anopheles arabiensis

Anopheles funestus

Indoors

Outdoors

Overall estimates

Indoors

Outdoors

Overall estimates

Total no. Anopheles caught (all traps)a

3543

4252

7795

252

148

400

Total no. Trap nights

1792

1780

3572

1792

1780

3572

Total no. Anopheles analyzed for CSP

3543

4252

7795

252

148

400

Total no. sporozoite-positive Anopheles

0

0

0

0.00

1

1

Annual PfEIR

0.00

0.00

0.00

0.00

0.205

0.102

% PfEIR contribution by species

0%

100%

Since all Anopheles collected in this survey were analysed for Plasmodium infection, no differences were expected between overall PfEIR estimations obtained by either the standard or the alternative method as described by Drakeley et al. [13]

aIn this table, only the alternative method is used to calculate PfEIR. The only sporozoite positive mosquito was that captured by human landing catches (HLC), so no adjustments on the other traps were done as they would have marginal effect on overall PfEIR estimates, which would be zero nonetheless. Instead, we have considered PfEIR estimates without any adjustments and assumed similar trap efficacies. If only outdoor An. funestus catches were considered, the PfEIR was estimated at double the overall, but these are still very low and only detectable marginally by HLC

Plasmodium infectious rates and malaria transmission intensities

All 8613 Anopheles mosquitoes were analysed for Plasmodium circumsporozoite protein (CSP) using ELISA. These included 7795 An. gambiae s.l. (all of which were also determined by PCR to be An. arabiensis) and 400 An. funestus (a majority of which were determined by PCR to be An. funestus s.s.). In these ELISA assay, all An. arabiensis were uninfected and only one An. funestus s.s. mosquito was found to be infected with Plasmodium. This infected specimen had been collected outdoors between 03:00 and 04:00 h in Katindiuka ward in August 2015. Coincidentally, this was also the ward with highest malaria vector densities (Table 3). Since all Anopheles collected in this survey were analysed for Plasmodium infection, PfEIR estimations by the standard and alternative methods [13] returned similar results (Table 6). Sporozoite-infected mosquitoes were detectable only by human landing catches, but not any of the other traps (Table 6). Overall PfEIR was 0.102 ib/p/yr.
Table 6

Comparative estimates of annual PfEIR using the two different methods [13], for malaria mosquitoes collected: indoors versus outdoors, in wet versus dry seasons, using different trapping methods, and in the different geographical zones of the study area

Attributes

Category

No. mosquitoes caught

No. trap nights (no. traps per night * no. nights)

Biting rate (no. mosquitoes/no. nights)

No. sporozoites positive

Sporozoite positive rate

PfEIR by standard methoda

PfEIR by alternative method a

Location

Indoors

3801

1792

2.12

0

0

0

0

Outdoors

4394

1780

2.47

1

0.00023

0.205

0.205

Season

Wet (December–May)

7604

1786

4.26

0

0

0

0

Dry (June–November)

591

1786

0.33

1

0.00169

0.204

0.204

Temperature

Cool (June–September)

706

1190

0.59

1

0.00142

0.307

0.307

Hot (October–May)

7489

2382

3.14

0

0

0

0

Zones

North and Western area (Viwanja Sitini and Ifakara Mjini)

853

1427

0.60

0

0

0

0

South and central areas (Lipangalala and Mlabani)

2379

1427

1.67

0

0

0

0

Eastern peri-urban areas (Katindiuka)

4963

714

6.95

1

0.00020

0.511

0.511

Trapping method

HLC

5197

1200

4.33

1

0.00019

0.304

0.304

CDC light trap

1894

1192

1.59

0

0

0

0

Suna trap

1104

1180

0.94

0

0

0

0

Overall

8195

3572

2.29

1

0.000122

0.102

0.102

aThe standard method calculates PfEIR as a product of sporozoites rates and human-biting rates; i.e., C*(Number of sporozoite positive mosquitoes/Number of mosquitoes tested) × (Number of mosquitoes collected/Number of trap nights) × 365(46), where, C is the relative efficiency coefficient for the relationship between CDC-light trap catches and human landing catches. The alternative method calculates PfEIR the ratio of sporozoite positive mosquitoes and number of nightly catches; i.e., C*(no of sporozoite positive mosquitoes/number of trap nights) × 365(14). Though the sporozoite rates are varied, the PfEIR estimates are same, primarily because all An. gambiae and An. funestus were analysed

Association between household characteristics and malaria vector densities

Of the 110 houses observed, 94.5% were constructed with brick walls and 5.5% had mud walls. Likewise, 97.3% of the houses had metal roofs and 2.7% had thatched roof. The houses had an average of 5.3 windows, ranging from 2 windows in small houses to 17 windows in the largest houses. The average number of outlet doors was 1.7, ranging from 1 to 7 doors. Half of the windows across the study area were covered with netting screens (51.8%), while the rest had wood or metal on the windows (17.3%), bricks (16.4%) or cardboard and clothes (13.6%). Only one house had completely uncovered windows (0.09%). The type of window cover had an effect on indoor densities of the primary malaria vectors, as lower indoor densities were observed in houses with netting screens on the windows compared to houses with other types of covers (Table 7).
Table 7

Indoor and outdoor densities of the malaria vector species, An. arabiensis and An. funestus mosquitoes in houses with different characteristics in Ifakara town and its adjacent wards

House characteristics

No. houses (N)

Mean no. An. arabiensis per house [LCI–UCI]

Mean no. An. funestus per house [LCI–UCI]

t-test P-value

Indoor mosquito densities (association of vector densities with electricity)

 Without electricity

53

59.1 [27.9–90.3]

4.63 [2.2–7.1]

<0.05

 With electricity

57

7.3 [4.6–10.0]

0.18 [− 0.03–0.4]

Outdoor mosquito densities (association of vector densities with electricity)

 Without electricity

53

67.2 [18.0–116.4]

2.6 [0.2–4.9]

<0.05

 With electricity

57

12.0 [1.7–22.6]

0.3 [0.1–0.5]

Indoor mosquito densities (association of vector densities with window covering)

 Windows covered with netting screen

57

14.8 [7.0–22.6]

1.6 [0.4–2.8]

<0.05

 Windows covered with wood or metal

19

24.0 [18.1–29.9]

0.8 [− 0.2–1.8]

 Windows covered with bricks

18

36.1 [11.5–83.7]

3.6 [− 1.3–8.5]

 Windows covered with cardboard/clothes

15

75.3 [4.3–146.3]

5.4 [0.2–10.7]

 Windows uncovered

1

471.0 [471.0–471.0]

3.0 [3.0–3.0]

All (100%) of households had at least one insecticide-treated net (ITN). It was also observed that 51.8% of the houses had electricity while 48.2% did not. Electricity was mostly in the Ifakara Mjini, Lipangalala and Viwanja Sitini wards. All the sampled houses in Katindiuka ward had no electricity, and only 7 of the 22 households in Mlabani had electricity. Most of the houses in the peri-urban ward of Katindiuka were also surrounded by small rice fields and water ponds, especially during the wet season while houses in the urban settings of Ifakara Mjini and Viwanja Sitini wards were generally surrounded by small businesses such as small shops, vegetable stalls and bodabodas (motorcycle taxis) (Fig. 5). Lower mosquito densities were observed in houses with electricity compared to houses without electricity (Table 7). A great deal of brick-making activities was also observed in the dry season which resulted in a lot of pits that may have provided adequate breeding habitats for mosquitoes. Lastly, only one household had cattle, but 49 of the households (44.5%) had chickens or ducks.

Discussion

Africa is the world’s most rapidly urbanizing continent [1, 2]. Currently about 40% of the African population resides in urban settings, but that proportion is expected to reach 56% by 2050 [1, 2]. Malaria, like most other infectious diseases, is negatively impacted by urbanization [710], since urban settings are characterized by better access to health care services, better housing and reduced availability of larval habitats [810]. In Tanzania, the urban population is increasing at 5.4% per annum. Tanzanian urban population was estimated at 5.7% in 1967 [15], but it had increased to 22.6% in 2002 [4], 29.1% in 2012 [51] and is estimated to be 33% in 2018 [3].

This current study focused on one of the fast-growing towns in Tanzania (i.e. Ifakara), and where demographic, socio-economic and epidemiological transitions are strongly evident. Ifakara town, in the Kilombero river valley is surrounded by rain forests, wetlands, savannah lands making it an attractive setting for both farmers and pastoralists. More importantly, as a town in the middle of an expansive low-lying flood-plain, only eight degrees south of the Equator, the area presents a unique epidemiological profile with very high malaria transmission in surrounding areas.

Though there have been several entomological surveys in the villages across the Kilombero valley over the past three decades [12, 5254], there have been limited investigations within the Ifakara town centre itself, or in its closely adjacent wards. The most recent examination of malaria transmission in the town area was completed more than 15 years ago by Drakeley and colleagues [13]. Over the years, since the study by Drakeley et al. in early 2000s, the area has experienced significant increases in population concurrent with urbanization, due to in-migration by small business owners, farmers and pastoralists (Mayor of the Ifakara Town Council—Personal Communication). This current study presents the first reassessment of transmission in this community in nearly two decades.

A year-long entomological surveillance of malaria vectors was conducted in the five administrative wards making up the Ifakara town, an area with a total population estimated at 70,000 people. The magnitude and biting patterns of main malaria vector species, the levels of ongoing transmission, as well as the key factors driving the transmission in the town were identified. A general observation was that densities of malaria vectors within the area was found to be much lower compared the vector densities usually observed in nearby rural villages on both sides of the Kilombero river [21, 22]. Overall PfEIR was estimated to be 0.102 ib/p/yr, which was entirely driven by An. funestus and only detectable by HLC. This was more than 99% decrease from the PfEIR reported by Drakeley et al. in 2003 from the same area. The PfEIR estimates are also much lower than those recently observed by Kaindoa et al. in the neighbouring rural villages of Ulanga district (PfEIR = 18.45), about 20 km south of Ifakara town [21]. Some of the factors that could be attributed to this dramatic decline could be urbanization [7, 8, 10], the universal bed net coverage campaigns that started in 2004 [17, 55], improved housing [56] as well as improved diagnosis and treatment [16]. More than 90% of the houses in this study area now have brick walls and metal roofs, more than 50% had screened windows, and all (100%) had at least one bed net (Finda et al., unpublished). Unfortunately, this study was limited by the lack of aerial maps for the study area from 15 years ago, which would have enabled more detailed comparison of housing developments in the area. Nonetheless, these improvements, coupled with the ongoing large-scale use of long-lasting insecticide-treated nets (LLINs) and other malaria control interventions, as well as the changes in potential mosquito breeding habitats, it can be expected that malaria transmission should plummet even further.

The ease of detecting Plasmodium circumsporozoite proteins in mosquitoes using simple ELISA assays and the simplicity of calculating entomological inoculation rates (PfEIR) has made this approach fairly popular for assessing malaria endemicity and transmission intensity [44, 57]. It requires only simple measures of mosquito biting rates and sporozoites positivity rates, and is therefore a more direct method of measuring transmission intensity compared to other measures such as parasite incidence or prevalence and has been used widely for assessing impact of vector control programs in Africa [57, 58]. This method estimates the number of bites by infectious mosquitoes per person per unit time. It is the product of the “human biting rate”—the number of bites per person per day by vector mosquitoes—and the fraction of vector mosquitoes that are infectious [44]. However, despite its widespread use, there have also been concerns that PfEIR estimates may be inconsistent, and that there can be large variabilities in PfEIR estimates from the same area [57, 58]. Such variations are heightened by the differences in methods of measurement [57, 58] and the lack of clear relationship between PfEIR estimates and malaria parasite prevalence of incidence rates in localities with low transmission intensities [59, 60]. Besides, at very low PfEIR ranges, available methods are imprecise and there are very slim chances of getting mosquitoes at a precise time when innoculation takes place [60]. Therefore, the very low PfEIR estimates observed here do not necessarily mean absense of local transmission, but rather the lack of effective measurement methods.

In this study, only the human landing catches method was able to detect sporozoites positive mosquitoes. On the contrary, neither the CDC light traps nor the Suna® traps caught any infected mosquito. This suggests the need for much more sensitive tools and approaches for measuring human exposure to malaria parasites in conditions such as this, where parasite densities in mosquitoes have become too low. Nevertheless, it has also been argued that if similar methods are used to consistently estimate transmission over time, such as this study has done, and if adequate stratification is performed, then interventions that drive malaria transmission to PfEIR < 1, could be effective in achieving local elimination [61]. In this survey, the overall PfEIR of 0.102 ib/p/yr was indeed far below the threshold of 1 ib/p/yr, beyond which sustained local efforts could lead to complete disruption of local malaria transmission.

Going forward, improved measurements are therefore a critical component of the malaria elimination agenda [62]. It will thus be essential to deploy improved measurement methods that can assess both the burden and the transmission of malaria pathogens in such situations so as to support further efforts for elimination. It would also be important to couple such an entomological survey with a malaria parasite prevalence study, using methods capable of detecting low-level parasitaemia so as to more comprehensively understand the residual malaria epidemiology in the area.

Within the Ifakara town, the highest density of malaria vectors was observed in Katindiuka and Mlabani (Fig. 6) wards, which were also the most rural of the five wards. Households in Katindiuka and Mlabani wards were surrounded largely by rice paddies and water ponds (Fig. 5), and nearly all the houses in these wards did not have electricity. Brick-making activities were fairly common in these areas in the dry season which resulted in a lot of pits with standing water in the rainy season, which may have provided adequate breeding habitats for mosquitoes. Although nearly all of the households surveyed were made with bricks and metal roof, majority of the households in Mlabani and Katindiuka lacked electricity, hence they were in the dark through most of the night, which may also have provided a suitable environment for host seeking mosquitoes [63].

In the study done by Drakeley et al, 91.5% of all An. gambiae s.l. were An. arabiensis while only 8.5% were An. gambiae s.s., none of which were sporozoites-positive. However, several studies done since the Drakeley et al. study have documented absence of An. gambiae s.s. from the Kilombero river valley, mainly attributable to the use of LLINs [22, 52, 54, 64]. While the most abundant malaria vector species was found to be An. arabiensis, it was An. funestus that was found to drive all of the transmission in the town. Only 400 An. funestus mosquitoes were collected compared to 7795 An. arabiensis, yet the only Plasmodium infected mosquito was an An. funestus. Though only a single infected An. funestus might be considered too few to conclude on the dominance of the species, evidence from neighboring villages suggest this is most likely the case. Similar patterns of dominance were indeed observed by Kaindoa et al. [21], who showed An. funestus to carry more than 80% of all the malaria transmission in neighboring villages in the Kilombero Valley. All blood-fed mosquitoes were found to contain human blood, and this can be explained by the absence of big livestock such as cows and goats in the urban settings, hence humans were the most available source of blood meals for the vectors. This could suggest that household-based interventions could still be effective in targeting malaria vectors in the Ifakara town. However, the actual blood-feeding proportions were relatively higher for An. funestus (i.e. 9% (36 out of 400) were blood-fed) compared to An. arabiensis for which 0.05% (38 out of 7795) were blood fed. This further demonstrates the importance of An. funestus.

Relatively high parity rates were observed; over three quarters of all the mosquitoes dissected were found to be parous, a fact that also emphasizes the need for interventions that prevent man–vector contact to limit local transmission. On the basis of parity rates and human blood index, both An. funestus and An. arabiensis can be considered important vectors in the area. However, based on actual contribution to PfEIR estimates, An. funestus is likely mediating most of the on-going residual transmission in both the wider valley and in Ifakara itself. In this study, An. funestus densities were also observed to be higher in the dry season, which is the time of the year that people are most relaxed with regards to protection against mosquitoes.

There was evidence of phenotypic resistance in An. arabiensis to permethrin, deltamethrin, lambda cyhalothrin, pirimiphos methyl and DDT. However, the same mosquito populations from the study villages were found 100% susceptible to bendiocarb and malathion. The most surprising aspect of this specific finding was that the organophosphate, pirimiphos methyl has not previously been used in the area for vector control. It is however widely used in agriculture (Matowo et al., pers.comm.), which could be the source of this resistance pressure exhibited here. Nonetheless, the resistance profile of the mosquitoes collected here is worrying as it signals that there is a very narrow set of insecticide options now available for malaria vector control. One minor limitation with this aspect of the study was that the resistance assays were conducted only in two of the five wards, primarily due to availability of Anopheles larvae at the time of the tests. These findings can however still be considered fairly representative of the whole study area, and concur with other studies that have been done in surrounding villages in the valley [21, 50].

Finally, the major decreases of malaria transmission seen are indeed impressive but should not be taken as a sign of impending local elimination. Instead, it should be interpreted as evidence that local elimination is possible given multisectoral approaches that combine house improvement to other technologies such as LLINs and effective case management, with proper diagnostics and medicines. The authors propose a parasitological survey to enable assessment of actual malaria cases prevalence and incidence rates, and also to examine in greater detail the actual rates of importation. Households should be encouraged to continue use of their long-lasting insecticide treated nets and to visit health facilities urgently whenever they experience any fevers. Given the significance of house improvement, these efforts too, should be encouraged to further reduce exposure to malaria and other mosquito-borne infections.

Conclusion

Malaria transmission intensity in Ifakara town and its surrounding environment has declined by over 99% over the past 15 years, reaching levels nearly undetectable with current entomological methods. Over a total of 3572 trap-nights, only one Plasmodium-infected Anopheles was found, an An. funestus mosquito caught outdoors in the peri-urban ward of Katindiuka. The overall PfEIR of 0.102 ib/p/yr is far below the threshold of 1 ib/p/yr, beyond which sustained local efforts could lead to complete disruption of local malaria transmission. This decline is likely associated with urbanization, improved housing, insecticide-treated nets and improved case management. House designs have vastly improved in the area and more than 90% of houses currently have brick walls and/or metal roofs, and more than half have screened windows. The remaining risk of Anopheles biting and malaria transmission in the valley is now mostly localized in the more rural ward adjacent to the town centre indicating that urbanization does indeed play a role in the control and possible elimination of malaria. On the basis of parity rates and human blood index, both An. funestus and An. arabiensis can be considered important vectors in the area. However, based on actual contribution to PfEIR estimates, An. funestus is likely mediating most of the on-going residual transmission in both the wider valley and in Ifakara itself, despite occurring in comparatively lower densities than An. arabiensis. Insecticide resistance has increased significantly, particularly against pyrethroids, organophosphate, pirimiphos-methyl and the organochloride, DDT, a situation which will clearly limit insecticidal options for malaria prevention. It is therefore possible that new cases reported in this area are now likely arising from infections outside the area than from within. It is also likely that concerted efforts could cause further decreases, possibly achieving complete disruption of local transmission in this area. Future surveys of malaria should deploy improved approaches for measuring transmission and parasite infection reservoirs in areas with such low transmission intensities. The authors specifically propose a parasitological survey to assess actual malaria prevalence and incidence rates, and estimate actual proportions of local and imported cases in the area.

Declarations

Authors’ contributions

MFF was involved in study design, data collection, entry and analysis, interpretation of the results and drafting of the manuscript. FOO was involved in study design supervision and manuscript revision. AJL was involved in study design, data collection and preparing maps for the study sites. HSN and JKS were involved in the statistical analyses for the study data. NSM was involved in conducting the resistance tests, and EWK generated land use maps Ifakara town between 2000 and 2016. All authors read and approved the final manuscript.

Acknowledgements

We would like to thank all the community leaders, community volunteers and heads of households in Ifakara town for allowing us to conduct this surveillance in their communities and homes for a whole year. We thank our research assistants; Masoud Kilalangongono, Joseph Mgando, Khamis Kifungo and late Godfrey Lingamba for their assistance with the field study. We also thank our colleague Arnold Mmbando for providing supervisory support of this work in the few cases when the lead researcher was absent.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All data for this study will be available upon request.

Consent for publication

Permission to publish this study was obtained from the National Institute for Medical Research (NIMR), ref: NIMR/HQ/P.12 VOL XXV/.

Ethics approval and consent to participate

Ethical approval was obtained from the Ifakara Health Institute’s Institutional Review Board (IHI/IRB/No: 06-2016), and the Medical Research Coordination Committee of the National Institute for Medical Research in Tanzania (MRCC) (NIMR/HQ/R.8a/Vol.IX/2218). All household heads, volunteers and field assistants were informed about the aim of the study, the risks involved and precautionary measures to be taken during the study. Written informed consent forms, which also contained detailed description of the study and the study procedures in local language, Kiswahili, were given to all the volunteering participants for overnight reading and familiarization. The next day, all volunteers agreeing to participate were required to sign these consent forms, prior to starting the experiment. Volunteers doing HLC were tested for malaria every month (during which they worked for not more than 5 nights) and were given malaria prophylaxis (250 mg Mefloquine taken orally) every week of their participation in the study. In the case that any volunteer contracted malaria any time during the study period, they were referred to a clinician and provided twith malaria treatment (Coartem®, 80 mg artemether and 480 mg lumefantrine for 3 days) free of charge. Additionally, all volunteers doing HLC were encouraged to wear long-sleeved shirts for extra protection against mosquito bites.

During the course of the study, with help from the community leaders, several community engagement activities were done to inform the community of this study. Activities done included participating in regular community meetings to inform the community members, and open days, where selected community members and leaders were invited to the Ifakara Health Institute to interact actively with scientists and visit research facilities. At the end of the study, all the 110 households were provided with solar-powered lamps, this having been identified as a key concern during our community engagement exercises.

Funding

This work was supported by the Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (Grant Number: WT102350/Z/13) awarded to FO. LF was also supported through a Consortium for Advanced Research Training (CARTA) grant awarded by Wellcome Trust (Grant No: 087,547/Z/08/Z), the Carnegie Corporation of New York [B 8606.R02], and Sida [54100029].

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Authors’ Affiliations

(1)
Environmental Health and Ecological Science Department, Ifakara Health Institute, P. O. Box 53, Ifakara, Tanzania
(2)
School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
(3)
University of Basel, Basel, Switzerland
(4)
Swiss Tropical and Public Health Institute, Basel, Switzerland
(5)
Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK

References

  1. United Nations Department of Economics and Social Affairs. World urbanization prospects—2014 revisions. United Nations. 2014. http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf. Accessed May 2018. 
  2. World Population Review. World population review: Tanzania. 2018. http://worldpopulationreview.com/countries/tanzania-population/. Accessed May 2018. 
  3. World Bank. World development indicators 2017. Washington, DC. 2017. https://openknowledge.worldbank.org/handle/10986/26447. Accessed May 2018. 
  4. National Bureau of Statistics. 2002 Population and housing census: population distribution by administrative areas. 2003.Google Scholar
  5. Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2016;526:207–11.View ArticleGoogle Scholar
  6. Hay SI, Guerra CA, Tatem AJ, Noor AM, Snow RW. The global distribution and population at risk of malaria: past, present, and future. Lancet Infect Dis. 2004;4:327–36.View ArticlePubMed CentralGoogle Scholar
  7. Tatem AJ, Gething PW, Smith DL, Hay SI. Urbanization and the global malaria recession. Malar J. 2013;12:133.View ArticlePubMed CentralGoogle Scholar
  8. Donnelly MJ, McCall P, Lengeler C, Bates I, D’Alessandro U, Barnish G, et al. Malaria and urbanization in sub-Saharan Africa. Malar J. 2005;4:12.View ArticlePubMed CentralGoogle Scholar
  9. Hay SI, Guerra CA, Tatem AJ, Atkinson PM, Snow RW. Urbanization, malaria transmission and disease burden in Africa. Nat Rev Microbiol. 2011;3:81–90.View ArticleGoogle Scholar
  10. Qi Q, Guerra C, Moyes CL, Elyazar IRF, Gething PW, Hay SI, et al. The effects of urbanization on global Plasmodium vivax malaria transmission. Malar J. 2012;11:403.View ArticlePubMed CentralGoogle Scholar
  11. Freyvogel TA, Kihaule PM. Report on a limited anopheline survey at Ifakara, South-Eastern Tanzania. Acta Trop. 1968;25:17–28.PubMedPubMed CentralGoogle Scholar
  12. Smith T, Charlwood JD, Kihonda J, Mwankusye S, Billingsley P, Meuwissen J, et al. Absence of seasonal-variation in malaria parasitemia in an area of intense seasonal transmission. Acta Trop. 1993;54:55–72.View ArticlePubMed CentralGoogle Scholar
  13. Drakeley C, Schellenberg D, Kihonda J, Sousa CA, Arez AP, Lopes D, et al. An estimation of the entomological inoculation rate for Ifakara: a semi-urban area in a region of intense malaria transmission in Tanzania. Trop Med Int Health. 2003;8:767–74.View ArticlePubMed CentralGoogle Scholar
  14. Agwanda A, Amani H. Population growth, structure, and momentum in Tanzania. Economic and Social Research Foundation; 2014. http://www.thdr.or.tz/docs/THDR-BP-7.pdf. Accessed May 2018. 
  15. Worrall L, Colenbrander S, Palmer I, Makene F, Mushi D, Kida T, et al. Better urban growth in Tanzania: a preliminary exploration of the opportunities and challenges. Coalition for urban transitions. 2017. http://www.esrf.or.tz/docs/NCE2017_Better_Urban_Growth_Tanzania_final.pdf. Accessed May 2018. 
  16. Alba S, Hetzel MW, Goodman C, Dillip A, Liana J, Mshinda H, et al. Improvements in access to malaria treatment in Tanzania after switch to artemisinin combination therapy and the introduction of accredited drug dispensing outlets—a provider perspective. Malar J. 2010;9:164.View ArticlePubMed CentralGoogle Scholar
  17. Kramer K, Mandike R, Nathan R, Mohamed A, Lynch M, Brown N, et al. Effectiveness and equity of the Tanzania National Voucher Scheme for mosquito nets over 10 years of implementation. Malar J. 2017;16:255.View ArticlePubMed CentralGoogle Scholar
  18. Mugittu K, Abdulla S, Falk N, Masanja H, Felger I, Mshinda H, et al. Efficacy of sulfadoxine–pyrimethamine in Tanzania after two years as first-line drug for uncomplicated malaria: assessment protocol and implication for treatment policy strategies. Malar J. 2005;4:55.View ArticlePubMed CentralGoogle Scholar
  19. Smithson P, Florey L, Salgado SR, Hershey CL, Masanja H, Bhattarai A, et al. Impact of malaria control on mortality and anemia among Tanzanian children less than five years of age, 1999–2010. PLoS ONE. 2015;10:e0141112.View ArticlePubMed CentralGoogle Scholar
  20. National Bureau of Statistics. 2012 population and housing census population distribution by administrative areas. NBS ministry of finance. 2013.Google Scholar
  21. Kaindoa EW, Matowo NS, Ngowo HS, Mkandawile G, Mmbando A, Finda M, et al. Interventions that effectively target Anopheles funestus mosquitoes could significantly improve control of persistent malaria transmission in south-eastern Tanzania. PLoS ONE. 2017;12:e0177807.View ArticlePubMed CentralGoogle Scholar
  22. Ngowo HS, Kaindoa EW, Matthiopoulos J, Ferguson HM, Okumu FO. Variations in household microclimate affect outdoor-biting behaviour of malaria vectors. Wellcome Open Res. 2017;2:102.View ArticlePubMed CentralGoogle Scholar
  23. Geubbels E, Amri S, Levira F, Schellenberg J, Masanja H, Nathan R. Health demographic surveillance system profile: the Ifakara rural and urban health and demographic surveillance system (Ifakara HDSS). Int J Epidemiol. 2015;44:848–61.View ArticlePubMed CentralGoogle Scholar
  24. John W. Hock company. CDC miniature light trap—Model 512. 2012. http://www.JohnWHock.com. Accessed Janury 2018. 
  25. Mboera LE, Kihonda J, Braks MA, Knols GG. Influence of centers for disease control light trap position, relative to a human-baited bed net, on catches of Anopheles gambiae and Culex quinquefasciatus in Tanzania. Am J Trop Med Hyg. 1998;59:595–6.View ArticlePubMed CentralGoogle Scholar
  26. Hiscox A, Otieno B, Kibet A, Mweresa CK, Omusula P, Geier M, et al. Development and optimization of the Suna trap as a tool for mosquito monitoring and control. Malar J. 2014;13:257.View ArticlePubMed CentralGoogle Scholar
  27. Okumu FO, Killeen GF, Ogoma S, Biswaro L, Smallegange RC, Mbeyela E, et al. Development and field evaluation of a synthetic mosquito lure that is more attractive than humans. PLoS ONE. 2010;5:e8951.View ArticlePubMed CentralGoogle Scholar
  28. Saitosh Y, Hattori J, Chinone S, Nihei N, Tsuda Y, Kurahashi H, et al. Yeast-generated CO2 as a convenient source of carbon dioxide for adult mosquito sampling. J Am Mosq Control Assoc. 2004;20:261–4.Google Scholar
  29. Detinova TS, Beklemishev WN, Bertram DS. Age-grouping methods in diptera, with special reference to some vectors of malaria. World Health Organ Monogr Ser. 1962;47:1–213.Google Scholar
  30. Gillies MT, De Meillon B. The Anophelinae of Africa south of the Sahara (Ethiopian Zoogeographical Region). Johannesburg: The South African Institute for Medical Research; 1968. p. 343.Google Scholar
  31. Gillies MT, Coetzee MA. Supplement to the Anophelinae of Africa South of the Sahara. Johannesburg: South African Institute for Medical Research; 1987. p. 143.Google Scholar
  32. Koekemoer LL, Kamau L, Hunt RH, Coetzee MT. A cocktail polymerase chain reaction assay to identify members of the Anopheles funestus (Diptera: Culicidae) group. Am J Trop Med Hyg. 2002;66:804–11.View ArticleGoogle Scholar
  33. Scott JA, Brogdon WG, Collins FH. Identification of single specimens of the Anopheles gambiae complex by the polymerase chain reaction. Am J Trop Med Hyg. 1993;49:520–9.View ArticleGoogle Scholar
  34. Beier JC, Perkins PV, Koros JK, Onyango FK, Gargan TP, Wirtz RA, Koech DK, Roberts CR. Malaria sporozoite detection by dissection and ELISA to assess infectivity of afrotropical Anopheles (Diptera: Culicidae). J Med Entomol. 1990;27:377–84.View ArticleGoogle Scholar
  35. Beier JC, Perkins PV, Koros JK, Diggs D, Gargan TP, Wirtz RA, Koech DK. Bloodmeal identification by direct enzyme-linked immunosorbent assay (ELISA), tested on Anopheles (Diptera: Culicidae) in Kenya. J Med Entomol. 1988;25:9–16.View ArticleGoogle Scholar
  36. Lochuarn L, Fontenille D. ELISA detection of malaria sporozoites: false-positive results in Anopheles gambiae s.l. associated with bovine bloodmeals. Trans R Soc Trop Med Hyg. 1999;93:101–2.View ArticleGoogle Scholar
  37. Bashar K, Tuno N, Ahmed T, Howlander A. False positivity of circumsporozoite protein (CSP)-ELISA in zoophilic Anophelines in Bangladesh. Acta Trop. 2013;125:220–5.View ArticleGoogle Scholar
  38. Durnez L, Van Bortel W, Denis L, Roelants P, Veracx A, Trung HD. False positive circumsporozoite protein ELISA: a challenge for the estimation of the entomological inoculation rate of malaria and for vector incrimination. Malar J. 2011;10:195.View ArticlePubMed CentralGoogle Scholar
  39. World Health Organization. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes. World Health Organ Tech Rep Ser. 2013;22:4.Google Scholar
  40. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2016. https://www.r-project.org/. Accessed May 2018. 
  41. Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.View ArticleGoogle Scholar
  42. Fournier DA, Skaug HJ, Ancheta J, Magnusson A, Maunder MN, Nielsen A. Optimization methods and software AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Methods Softw. 2012;27:233–49.View ArticleGoogle Scholar
  43. Whickham H. Elegant graphics for data analysis. Berlin: Springer; 2009.Google Scholar
  44. Burkot TR, Graves PM. The value of vector-based estimates of malaria transmission. Ann Trop Med Parasitol. 1995;89:125–34.View ArticleGoogle Scholar
  45. Charlwood J, Smith T, Kihonda J, Heiz B, Billingsley P, Takken W. Density independent feeding success of malaria vectors in Tanzania. Bull Entomol Res. 1995;85:29–35.View ArticleGoogle Scholar
  46. Okumu FO, Kihonda J, Mathenge E, Kotas ME, Moore SJ, Killeen GF. Comparative evaluation of methods used for sampling malaria vectors in the Kilombero Valley, South Eastern Tanzania. Open Trop Med J. 2008;1:51–5.View ArticleGoogle Scholar
  47. Briët OJT, Huho BJ, Gimnig JE, Bayoh N, Seyoum A, Sikaala CH. Applications and limitations of Centers for Disease Control and Prevention miniature light traps for measuring biting densities of African malaria vector populations: a pooled-analysis of 13 comparisons with human landing catches. Malar J. 2015;14:247.View ArticlePubMed CentralGoogle Scholar
  48. Getis A, Ord J. The analysis of spatial association by use of distance statistics. Geogr Anal. 1992;24:189–206.View ArticleGoogle Scholar
  49. Getis A, Ord J. Spatial analysis and modeling in a GIS environment. In: McMaster R, Usery EL, editors. A research agenda for geographic information science, chapt. 6. Boca Raton: CRC Press; 1996. p. 157–96.Google Scholar
  50. Matowo NS, Munhenga G, Tanner M, Coetzee M, Feringa WF, Ngowo HS, et al. Fine-scale spatial and temporal heterogeneities in insecticide resistance profiles of the malaria vector, Anopheles arabiensis in rural south-eastern Tanzania. Wellcome Open Res. 2017;2:96.View ArticlePubMed CentralGoogle Scholar
  51. National Bureau of Statistics. 2012 Population and housing census; Population distribution by administrative areas. Natl Bur Stat; 2013.Google Scholar
  52. Kaindoa EW, Mkandawile G, Ligamba G, Kelly-Hope LA, Okumu FO. Correlations between household occupancy and malaria vector biting risk in rural Tanzanian villages: implications for high-resolution spatial targeting of control interventions. Malar J. 2016;15:199.View ArticlePubMed CentralGoogle Scholar
  53. Killeen GF, Tami A, Kihonda J, Okumu FO, Kotas ME, Grundmann H, et al. Cost-sharing strategies combining targeted public subsidies with private-sector delivery achieve high bednet coverage and reduced malaria transmission in Kilombero Valley, southern Tanzania. BMC Infect Dis. 2007;7:121.View ArticlePubMed CentralGoogle Scholar
  54. Russell TL, Govella NJ, Azizi S, Drakeley CJ, Kachur SP, Killeen GF. Increased proportions of outdoor feeding among residual malaria vector populations following increased use of insecticide-treated nets in rural Tanzania. Malar J. 2011;10:80.View ArticlePubMed CentralGoogle Scholar
  55. Renggli S, Mandike R, Kramer K, Patrick F, Brown NJ, McElroy PD, et al. Design, implementation and evaluation of a national campaign to deliver 18 million free long-lasting insecticidal nets to uncovered sleeping spaces in Tanzania. Malar J. 2013;12:85.View ArticlePubMed CentralGoogle Scholar
  56. Lindsay SW, Jawara M, Paine K, Pinder M, Walraven GEL, Emerson PM. Changes in house design reduce exposure to malaria mosquitoes. Trop Med Int Health. 2003;8:512–7.View ArticlePubMed CentralGoogle Scholar
  57. Shaukat AM, Breman JG, McKenzie FE. Using the entomological inoculation rate to assess the impact of vector control on malaria parasite transmission and elimination. Malar J. 2010;9:122.View ArticlePubMed CentralGoogle Scholar
  58. Kelly-Hope LA, McKenzie FE. The multiplicity of malaria transmission: a review of entomological inoculation rate measurements and methods across sub-Saharan Africa. Malar J. 2009;8:19.View ArticlePubMed CentralGoogle Scholar
  59. Smith TA, Leuenberger R, Lengeler C. Child mortality and malaria transmission intensity in Africa. Trends Parasitol. 2001;17:145–9.View ArticleGoogle Scholar
  60. Beier JC, Killeen GF, Githure JI. Entomologic inoculation rates and Plasmodium falciparum malaria prevalence in Africa. Am J Trop Med Hyg. 1999;61:109–13.View ArticlePubMed CentralGoogle Scholar
  61. Ulrich JN, Naranjo DP, Alimi TO, Müller GC, Beier JC. How much vector control is needed to achieve malaria elimination? Trends Parasitol. 2013;29:104–9.View ArticlePubMed CentralGoogle Scholar
  62. The malERA Refresh Consultative Panel on Tools for Malaria Elimination. malERA: an updated research agenda for diagnostics, drugs, vaccines, and vector control in malaria elimination and eradication. PLoS Med. 2017;14:e1002455.View ArticleGoogle Scholar
  63. Sheppard AD, Rund SSC, George GF, Clark E, Acri DJ, Duffield GE. Light manipulation of mosquito behaviour: acute and sustained photic suppression of biting activity in the Anopheles gambiae malaria mosquito. Parasit Vectors. 2017;10:255.View ArticlePubMed CentralGoogle Scholar
  64. Kaindoa EW, Ngowo HS, Limwagu A, Mkandawile G, Kihonda J, Masalu JP, et al. New evidence of mating swarms of the malaria vector, Anopheles arabiensis in Tanzania. Wellcome Open Res. 2017;2:88.View ArticlePubMed CentralGoogle Scholar

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